diff --git a/AI/Day01/1 - python/Python.ipynb b/AI/Day01/1 - python/Python.ipynb new file mode 100644 index 0000000..9216acb --- /dev/null +++ b/AI/Day01/1 - python/Python.ipynb @@ -0,0 +1,1084 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Python3](./images/python.jpg)\n", + "\n", + "# Python\n", + "\n", + "During this first preliminary activity, you will learn the basics of Python.\\\n", + "Python is vast and we will only look at the most important notions of the language.\\\n", + "Therefore, it is more than likely that during this week, you will observe a notion that is not present in this activity.\\\n", + "In which case you will have to look for the solution yourself.\n", + "\n", + "[Python Documentation](https://docs.python.org/3/)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Introduction\n", + "\n", + "Python is a high-level, interpreted, interactive and object-oriented scripting language. Python is designed to be highly readable.\n", + "It uses English keywords frequently where as other languages use punctuation, and it has fewer syntactical constructions than other languages.\n", + "Here are some of the most important features of Python:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Types\n", + "\n", + "How to manage the types in python is a bit different than in other languages like C or Java for example:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "# First you don't need to declare variables or function before using them\n", + "# Python is dynamically typed, so you don't need to specify the type of variable\n", + "\n", + "my_var = 1 # my_var is an integer\n", + "print(my_var, type(my_var), end='\\n')\n", + "\n", + "my_var = 1.0 # my_var is now a float\n", + "print(my_var, type(my_var), end='\\n')\n", + "\n", + "my_var = \"Hello world\" # my_var is now a string\n", + "print(my_var, type(my_var), end='\\n')\n", + "\n", + "my_var = [1, 2, 3] # my_var is now a list\n", + "print(my_var, type(my_var), end='\\n')" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "To declare a function you just need to use the keyword def" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "def my_func():\n", + " print(\"Hello world\")\n", + "my_func() # you can put in comment this line to see the difference" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "You can also force the type of a variable.\n", + "And in a function you can force a variable to have value if not call in a function and force the return type of that function" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "def my_func2(repetition, end_sentence, string : str = \"Hello world\\n\", show_a_new_mechanic=0) -> str:\n", + " result = \"\"\n", + " for i in range(repetition):\n", + " result += (string)\n", + " result += end_sentence\n", + " print(show_a_new_mechanic)\n", + " return result" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "Now we will see a bit all things who can happen with a that kind of function" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "print(my_func2(2, \"end\",show_a_new_mechanic=\"You are doing great !\")) # You can call a varrialbe by his name" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": [ + "print(my_func2(2, \"end\", \"You are uncredible\\n\", 1)) # You don't need if you don't want to, but you need to respect the order" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": [ + "print(my_func2(2, \"end\")) # You can also call just the necessary variable" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": [ + "print(my_func2(\"That will be an error, it's important to give a type\", \"end\")) # The error is here because the first variable should be an integer" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": [ + "print(my_func2(2, \"end\", 1)) # The error is here because the third variable should be a string" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": [ + "print(my_func2(0, 3)) # The error is here because the return type should be a string" + ], + "metadata": { + "collapsed": false + }, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Strings\n", + "\n", + "In Python, strings are arrays containing smaller strings which represent characters.\n", + "\n", + "For example, by using the `type()` method we learned about earlier, you'll notice that `\"apple\"` and `'a'` are both of the same data type:\n", + "\n", + "> In Jupyter Notebooks, you can run each cell of code by clicking the ▶️ button or by pressing `SHIFT+ENTER` on your keyboard." + ] + }, + { + "cell_type": "code", + "metadata": { + "pycharm": { + "is_executing": true + } + }, + "source": [ + "this_is_a_string = \"apple\"\n", + "\n", + "type(this_is_a_string)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": { + "pycharm": { + "is_executing": true + } + }, + "source": [ + "this_is_also_a_string = this_is_a_string[0]\n", + "\n", + "type(this_is_also_a_string)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> As you can see, the output of `type()` is displayed beneath the cell.\n", + "> However, if you had both `type()` calls in the same cell, it would only display the last one so you would need to use `print()` if you wish to avoid creating multiple Jupyter cells." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conditions and loops \n", + "Because strings are arrays, multiple operations can be applied to them, like looping !\n", + "\n", + "```py\n", + "string = \"hello world\"\n", + "\n", + "for i in range(len(string)):\n", + " print(string[i])\n", + "```\n", + "\n", + "```py\n", + "for character in string:\n", + " print(character)\n", + "```\n", + "\n", + "These two blocks of code achieve the same result but they do so by different means.\n", + "\n", + "In the first example, we use an **iterable object** (we will learn how to make our own later) called `range` which contains an iterable from the provided arguments which we can use for our loop. Here, we provide the `len`gth of our `string` so range returns an iterable the size of `len`.\n", + "\n", + "In the second example, we pick each value from the `string` array inside a variable we chose to name `character` and then print it. You can do the same with any array." + ] + }, + { + "cell_type": "code", + "metadata": { + "pycharm": { + "is_executing": true + } + }, + "source": [ + "my_range = range(len(\"Hello world\"))\n", + "print(type(my_range), my_range, sep='\\n')\n", + "print(type(iter(my_range)), iter(my_range), sep='\\n')" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's say you want to print the \"Hello world\" string but you only want a new line when there is a space between the two words:\n", + "\n", + "```\n", + "Hello\n", + "world\n", + "```\n", + "\n", + "You can use an `if else` statement:" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "string = 'hello world'\n", + "\n", + "for c in string:\n", + " if c == ' ': # no need for (parantheses) in python\n", + " print() # the print() method prints a new line by default\n", + " else:\n", + " print(c, end='') # thankfully, you can overwrite the 'end' argument" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practice: Fizzbuzz\n", + "\n", + "This wouldn't be a pool if we only showed you cool stuff so it's time for you to use what you've learned so far to code the `Fizzbuzz` algorithm in Python.\n", + "\n", + "**Exercice :**\n", + "\n", + "Display the numbers **from 1 to nb_iterations** with a **for** loop.\\\n", + "If a number is a **multiples of 3**, write **\"Fizz\" instead of the number.**\\\n", + "If a number is a **multiples of 5**, write **\"Buzz\" instead of the number.**\\\n", + "If a number is a **multiple of both 3 and 5**, write **\"FizzBuzz\" instead of the number.**\n", + "\n", + "You must follow the **diagram** :\n", + "\n", + "![schema](./images/diagramme.png)" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "### enter your code here:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "###" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lists, tuples, sets and dictionaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are four different built-in data types which are used to store groups of data in Python, they are Lists, Tuples, Sets and Dictionaries.\n", + "\n", + "| |List |Tuple |Set|Dictionary\n", + "|-------------------|-----|------|---|---------|\n", + "|`new_instance =` |`[]` or `list()`|`()` or `tuple()`|`set()`|`{}` or `dict()`|\n", + "|Mutable |✅|❌|✅|✅\n", + "|Ordered |✅|✅|❌|✅\n", + "|Allows duplicates |✅|✅|❌|❌" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We've made a series of operations to each of these data types, analyse each of these cells to see what operations were made and why each result " + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "my_list = list()\n", + "my_list.append(1) ## adding '1' to our list\n", + "my_list.append(1) ## adding '1' again to our list\n", + "my_list.append(2) ## adding '2' to our list\n", + "my_list.pop() ## removing the last element from our list\n", + "\n", + "my_list" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> You can also provide an index to `pop()` in order to remove a value at a certain index" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "my_tuple = tuple(my_list) # once a tuple is defined, you can no longer modify its contents\n", + "\n", + "my_tuple" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "my_set = set()\n", + "my_set.add(1) # adding '1' to our set\n", + "my_set.add(1) # adding '1' again to our set\n", + "my_set.add(2) # adding '2' to our set\n", + "my_set.remove(2) # removing the last element from our list (since sets are unordered, we remove '2' manually because we know it is the last element we added)\n", + "\n", + "my_set" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "my_dict = dict()\n", + "my_dict[1] = \"one\" # adding '1' to our dictionary\n", + "my_dict[1] = \"ONE\" # adding '1' again to our dictionary (we also changed its value)\n", + "my_dict[2] = \"two\" # adding '2' to our dictionary\n", + "my_dict.popitem() # removing the last element from our list\n", + "\n", + "my_dict" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although dictionaries are ordered since python version 3.7, `popitem()` might not be the most useful method when dealing with dictionaries, so keep in mind that you can delete any dictionary entry by using `del my_dict[key]`:\n", + "\n", + "```py\n", + "my_dict = {}\n", + "my_dict[\"apple\"] = \"red\"\n", + "my_dict[\"banana\"] = \"yellow\"\n", + "\n", + "del my_dict[\"apple\"] ## this will delete the \"apple\" key\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practice: Occurences in sentence\n", + "\n", + "With all of this, you should be able to create a dictionary containing the amount of times each character makes an appearance inside a given string.\n", + "\n", + "For example, with \"Hello world\", your dictionary should be:\n", + "\n", + "```\n", + "{\n", + " \"h\": 1,\n", + " \"e\": 1,\n", + " \"l\": 3,\n", + " \"o\": 2,\n", + " \" \": 1,\n", + " \"w\": 1,\n", + " \"r\": 1,\n", + " \"d\": 1,\n", + "}\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "sentence = \"By using what you've learned about dictionaries, this exercise should not be too difficult. Good luck !\"\n", + "\n", + "# Enter your code here\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "#" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Functions & Classes\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before heading any further, you might want to know how to make a function (also called method) in Python.\n", + "\n", + "It's as simple as this:\n", + "\n", + "```py\n", + "def myFunc(arg1, arg2): # as Python is dynamically typed, there is no need to specify argument types\n", + " my_code = arg1 + arg2\n", + " return my_code # you can also return multiple values if you want: simply seperate them using commas\n", + "```\n", + "myFunc(1, 2) # For calling the method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why don't you try wrapping your occurence counter exercise from before inside a method ?" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "def occurenceCounter(sentence):\n", + " \"\"\" \n", + " Copy paste your code here and make the necessary adjustments\n", + " \"\"\"\n", + " my_dict = {}\n", + "\n", + "\n", + " return my_dict\n", + "\n", + "occurenceCounter(\"Wow, making functions is really easy in Python !\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Classes is what we call **Object Oriented Programming** (OOP) and is essential in a vast number of languages.\n", + "To vulgarize, classes are sort of mold used to create **object**. Once you've the molds, you can create as many objects of the same type as you want. This is used in every import you do, any functions from libraries are **methods** from classes.\n", + "Their names are often written with a uppercase at the beginning. \n", + "\n", + "If you want to get deeper in this notion,\\\n", + "I highly recommend you to search on Internet. It's a well explained subject.\\\n", + "[Python classes doc](https://docs.python.org/3/tutorial/classes.html)\n", + "\n", + "**Example :**\\\n", + "Here is an example of a class, to help understand here are some remarks on the code :\n", + "\n", + "- variables that start with __ are called private\n", + "- the method \\_\\_init__ is the constructor, it is called at the instanciation of the object.\n", + "- the method \\_\\_str__ is a method that describes the object." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "class MyClass:\n", + " '''This is my first class in Python'''\n", + " def __init__(self, name, firstname, fav_color, fav_digit):\n", + " self.name = name\n", + " self.firstname = firstname\n", + " self.setFavColor(fav_color)\n", + " self.setFavDigit(fav_digit)\n", + " \n", + " def __str__(self):\n", + " return f'Your name is {self.firstname} {self.name}, your favorite color is {self.getFavColor()} and your favorite number is {self.getFavDigit()}'\n", + " \n", + " def setFavColor(self, fav_color):\n", + " color = [\"red\", \"blue\", \"purple\", \"green\", \"yellow\", \"orange\", \"white\", \"black\", \"pink\", \"brown\"]\n", + " if fav_color in color:\n", + " self.__fav_color = fav_color\n", + " else:\n", + " self.__fav_color = None\n", + " \n", + " def setFavDigit(self, fav_digit):\n", + " if isinstance(fav_digit, int) and -1 < fav_digit < 10:\n", + " self.__fav_digit = fav_digit\n", + " else:\n", + " self.__fav_digit = None\n", + " \n", + " def getFavDigit(self):\n", + " return self.__fav_digit \n", + " \n", + " def getFavColor(self):\n", + " return self.__fav_color \n", + " \n", + "robot = MyClass(\"Robot\", \"PoC\", \"red\", 5)\n", + "print(robot)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercice :**\\\n", + "Create a ```Calculator``` class.\n", + "\n", + "It will take as initialization parameter a ```name``` value.\\\n", + "it will have the methods ```add```, ```sub```, ```mul```, ```div```, ```modulo``` which will take two parameters ```x``` and ```y``` and will return the result of the operation corresponding to the name of each method between x and y.\\\n", + "Create a method ```__str__``` that will return ```Hello my name is {name}.```" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Create your Calculator class here\n", + "\n", + "class Calculator:\n", + " pass\n", + "\n", + "#\n", + "\n", + "\n", + "my_calc = Calculator(\"PoC\")\n", + "print(my_calc)\n", + "print(my_calc.add(1, 2))\n", + "print(my_calc.mul(1, 2))\n", + "print(my_calc.sub(1, 2))\n", + "print(my_calc.div(1, 2))\n", + "print(my_calc.modulo(1, 2))" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What if you now wanted to create a new Class which reuses the methods inside the Calculator class ?" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "class SuperCalculator(Calculator):\n", + " def __init__(self, name):\n", + " super().__init__(name) # the super() keyword inherits all the parameters of the parent class...\n", + "\n", + " def square(self, x):\n", + " return self.mul(x, x) # ...which allows you to call its methods inside SuperCalculator\n", + "\n", + "my_super_calc = SuperCalculator(\"Hello world\")\n", + "\n", + "my_super_calc.square(3)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Anonymous Function\n", + "\n", + "### 📖 A bit of history...\n", + "\n", + "Python 1.0 introduced functional programming tools such as `lambda`, `map`, and `filter` (the latter two will be covered together in the next section, cf: \"Array methods\"). These features were added by a Python user who found that the language was incomplete without them.\n", + "\n", + "### The λ lambda function\n", + "\n", + "One of these features, the lambda function provides Python developers the ability to use anonymous functions:" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "hello_world = lambda: print(\"hello, world\") ### simply define the function after 'lambda:'\n", + "\n", + "hello_world()" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "square = lambda x: x ** 2 ### you can provide arguments to lambda (you can call `x` whatever you want)\n", + "\n", + "square(5)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "add = lambda a, b: a + b ### you can give lambda as many arguments as you want\n", + "\n", + "add(2, 3)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### List comprehensions\n", + "\n", + "Similarly to lambda functions, you can replace `for loops` with **list comprehensions** to quickly apply a function to any list:" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "my_array = [1, 2, 3, 4, 5, 6]\n", + "\n", + "[x * 10 for x in my_array] # again, you can call `x` whatever you want" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Array methods\n", + "\n", + "Lambda is very powerful when used with some awesome methods in python for dealing with arrays that every person learning Python should be familiar with !\n", + "\n", + "In this section we'll introduce \n", + "- `map()`, for **map**ping through an array and transforming all of its values at once\n", + "- `filter()`, for **filter**ing an array's values, allowing you to keep only values which match a condition\n", + "\n", + "They both take a function as first argument and an array as the second argument, so you can use `lambda` functions directly !" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Try and implement `map()` and `filter()` in the below cell:\\\n", + "> You should use `lambda` to make your life easier" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "my_array = [\"Mapping's\", \"actually\", \"powerful\"]\n", + "\n", + "my_mapped_array = None ## use+ to transform my_array into an array containing only the first letter of each word\n", + "print(list(my_mapped_array))\n", + "\n", + "my_array = [1, 2, 3, 4, 5, 6]\n", + "\n", + "my_filtered_array = None ## use filter to keep only the even numbers inside my_array\n", + "\n", + "print(list(my_filtered_array))" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try, except, raise, assert: **Error Handling in Python**\n", + "\n", + "If you attempted to call the methods before defining them inside the Class, you might've run into some errors, like for example:\n", + "\n", + "```\n", + "AttributeError: 'Calculator' object has no attribute 'add'\n", + "```\n", + "\n", + "There is a way for you to handle such errors in Python !\n", + "\n", + "> For this section, we *will* be dealing with errors, so don't worry if you see a lot of red outputs in your notebook, this is the only place where it will mean the code is executing properly :)" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "def try_division(number1, number2):\n", + " try: ## we make an attempt to run the code\n", + " ans = my_calc.div(number1, number2)\n", + " except ZeroDivisionError: # if the code encounters a ZeroDivsionError error\n", + " print(\"You cannot divide a number by zero !\")\n", + " except: # if the code encounters any other error\n", + " print(\"An error has occurred\")\n", + " else: # if the code does not encounter an error\n", + " print(f\"Okay, no rules were violated: the answer is {ans}\")\n", + " finally: # regardless of result\n", + " print(f\"({type(number1)} {number1}, {type(number2)} {number2})\")\n", + " print()\n", + "\n", + "try_division(2, 0)\n", + "try_division(2, \"two\")\n", + "try_division(2, 2)" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> You might notice that we used a cool trick to format our printed messages:\\\n", + "> You can add variables to your print commands by adding the 'f' character inside the method call !\n", + "```py\n", + ">>> print(f\"This print statement contains {(int)(9 / 9 + 1 - 1)} variable inside curly brackets !\")\n", + "This print statement contains 1 variable inside curly brackets !\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> Another cool trick here: if we didn't use (int) to cast our result from a float into an int, our sentence would have read \"... contains 1.0 variable ...\" which would have been weird.\n", + "\n", + "You can also use the `assert` keyword to make sanity checks in order to test your Python code:" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "assert 1 == 1, \"one is not equal to one...\" ## this assert will pass because 1 == 1\n", + "assert 1 == 2, \"one is not equal to two...\" ## this will raise an AssertionError because 1 != 2" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "def signUp(username):\n", + " if username == \"PoCInnovation\":\n", + " raise Exception(f\"{username} is already taken\")\n", + " print(f\"Welcome, {username} !\")\n", + "\n", + "signUp(\"PoCCommunity\")\n", + "signUp(\"PoCInnovation\")" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practice: Custom Exception\n", + "\n", + "Let's put all of this into practice: you've learned about class inheritance and exceptions... what if you made your own custom exception by **inheriting** the Exception class ?\n", + "\n", + "This is the output you should receive:\n", + "\n", + "```\n", + "----> 8 raise MyException(\"This is my custom Exception\")\n", + "MyException: This is my custom Exception\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Enter your code here\n", + "\n", + "#" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading from a file\n", + "\n", + "Although there are awesome libraries in python for data reading, like **Pandas**, we will be doing it the old fashioned way for just a little bit longer ! But don't worry, later today, you will start using the most popular tools in artifcial intelligence for data analysis !\n", + "\n", + "### `with` keyword\n", + "\n", + "In python the with keyword is used when working with unmanaged resources (like file streams).\\\n", + "It is similar to the use statement in VB.NET and C#.\\\n", + "It allows you to ensure that a resource is \"cleaned up\" when the code that uses it finishes running, even if exceptions are thrown. " + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "with open('./data_types.txt', 'r') as f:\n", + " data = f.read() ### read() will read all the content inside the file\n", + "\n", + "data" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "with open('./data_types.txt', 'r') as f:\n", + " lines = f.readlines() ## readlines() will read the file line by line and return a list of each line\n", + "\n", + "lines" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practice: Auto correction\n", + "\n", + "Today's first assignment was to fill the data types for each of the following values:\n", + "\n", + "```\n", + "1\n", + "\"hello world\"\n", + "1.0\n", + "[\"apples\", \"oranges\", \"bananas\"]\n", + "{\"answer\": 42}\n", + "2 + 2 == 4\n", + "None\n", + "```\n", + "\n", + "inside a file called 'data_types.txt'\n", + "\n", + "With everything you've learned, we want you to verify if you've done your own assignment properly !\n", + "\n", + "What is required:\n", + "\n", + "- create a collection of your choice which will contain the required values listed above\n", + "- loop through the values and create a new dictionary with their `type()` as value\n", + "- open your own 'data_types.txt' file and see if the contents match with the dictionary\n", + "- use error handling methods like exceptions or asserts to verify if your 'data_types.txt' file's content is correct\n", + "\n", + "**Example:**\n", + "\n", + "If your dictionary contains : {1: 'int'} and the 'data_types.txt' file doesn't read `int` as it's first line, an error must occur !" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "# Write your code inside this cell\n", + "# The only requirement is that a method called verify_data_types() exists\n", + "# Feel free to make any other changes\n", + "\n", + "filename = './data_types.txt'\n", + "\n", + "def verify_data_types(filename):\n", + " pass" + ], + "outputs": [], + "execution_count": null + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Writing to a file (and using libraries)\n", + "\n", + "You now have a nice dictionary with each value and its type.\n", + "\n", + "The way we filled in the values inside 'data_types.txt' is kind of ugly...\n", + "\n", + "In data science, a file extension that is commonly used is '.csv'.\n", + "\n", + "A '.csv' file is formatted as follows:\n", + "\n", + "```\n", + "column_a, column_b\n", + "index1_a, index2_b\n", + "index2_a, index2_b\n", + "```\n", + "\n", + "In our case, it would look like:\n", + "\n", + "```\n", + "value, data type\n", + "1, int\n", + "\"hello world\", str\n", + "```\n", + "\n", + "For the last assignment in this first notebook, we will ask you to please fill in a file called 'data_types.csv' the rest of the values in the **csv** format.\n", + "\n", + "> You will need to use `with open('./data_types.csv', 'w') as f:` because you are **w**riting to a file, not **r**eading.\n", + "\n", + "In order to make things easier for you, there is a library called `csv`, which, as the name suggests, provides various methods and Classes which are useful for managing csv files in python !\n", + "\n", + "To import `csv` (and any other package in python), you simply need to run the following cell:" + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "import csv" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, you have access to any method or class defined inside `csv` by calling it with `csv.[methodName]()`\n", + "\n", + "If your IDE supports it, you can also start typing `csv.` and the autocomplete might have some suggestions for you.\\\n", + "If not, check out the [official documentation](https://docs.python.org/3/library/csv.html)." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "## Enter your code here\n", + "\n", + "\n", + "\n", + "##" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Great job ! You now master the basics of the python language ! 🥳\n", + "\n", + "You can now start using external packages which will prove very useful for data science and machine learning in general !" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day01/1 - python/README.md b/AI/Day01/1 - python/README.md new file mode 100644 index 0000000..e9b18c4 --- /dev/null +++ b/AI/Day01/1 - python/README.md @@ -0,0 +1,180 @@ +# Module 1: Python + +Welcome and thank you for participating in the Artificial Intelligence Pool by PoC Innovation ! Our team has worked hard to create a great learning resource and introduction to the incredible world of Artificial Intelligence and what better way to start than the most popular programming language for Machine Learning - **Python** ! + +During this week, you will use Python to tackle Machine Learning subjects of increasing difficulty, so never hesitate to ask the PoC staff for help, they'll be happy to answer your questions and don't worry about not completing each Pool Day: **the subjects are difficult to finish by design** ! + +Ultimately, the goal is for you to achieve a good knowledge of the field of AI after this week. So make sure you take your time to truly grasp the concepts you will see throughout this course. **Learning AI is not something you can rush; so make sure you understand the theory behind what you do during this Pool**. + +Now, buckle up: **your adventure begins now** ! + + +## Python3 + +Python is a high-level interpreted language, which has become very popular in the academic and scientific community for its simplicity and its layer of abstraction of several rules of computer science, such as the fact that you don't need to type your variables. Python is also able to interpret different programming paradigms like imperative, object-oriented... + +So you may ask yourself: Why do we use Python which is slower than C/C++ for AI, a domain that requires a lot of resources? +Well, quite simply, libraries like TensorFlow work with a Python interface on the surface but use the C++ language behind. + +### Example + +> "Hello World" Function + +```py +def myFunc(name="Undefined"): +""" A function is defined using the keyword `def` followed by a space +and the name of the function, between brackets we put the different parameters +for the example we have the parameter `name` which by default is equal to Undefined. +This comment is called a docstring: it provides a way for developers to explain the usage of a function / class / etc. so that other developers can understand how to use them. For a function, you'll generally find the required arguments as well as the returned values and maybe some usage examples. + +Args: + name: name of the user + +Returns: + None +""" + print("Hello " + name) + +# myFunc() will display "Hello Undefined" +# myFunc("PoC") will display "Hello PoC" + +``` + +## Python Command Line + +Before we get started with **iPython notebooks**, which you will be using for most of the week, we'd like to show you a cool party trick that you can do using any terminal with Python installed ! + +Open up a new terminal window: inside, simply run the `python` command ! + +```bash +> python +``` + +This command will open up the python command line: + +```bash +> python +Python 3.X.XX (main, XXX XX XXXX, XX:XX:XX) +[GCC 7.5.0] :: Anaconda, Inc. on linux +Type "help", "copyright", "credits" or "license" for more information. +>>> +``` + +The output **will** differ based on your Python version and other variables but it **should** open a command line where you can execute any python code ! + +That's right: you can even build an entire neural network inside this command line (although we **will** be very scared if you choose to do so) + +Of course, you will not be writing your code inside this command line very often, because most of the time, you wish to save your code inside a `.py` or `.ipynb` file 😄. + +However, this command line can help you troubleshoot your code in a smaller scope. + +You can try using the `print()` method to print some stuff on your terminal. + +```bash +Type "help", "copyright", "credits" or "license" for more information. +>>> print("Hello world") +Hello world +>>> +``` + +Pretty cool, right ? What if you instead want to know what the result of 2 + 2 is ? + +```bash +Type "help", "copyright", "credits" or "license" for more information. +>>> print(2 + 2) +4 +>>> a = 2 + 2 +>>> print(a) +4 +>>> 2 + 2 +4 +>>> +``` + +Interesting... these three inputs have the same output, "4". + +One of them uses the `print()` method, the other stores 2 + 2 inside a and then prints it and the last one only calculates 2 + 2 but still, the output is 4 despite the fact that we didn't even ask Python to display the result ! + +Try to open your own Python command line and run your own experiments to see what's happening. + +In python, the result of each line of code is displayed in the console. This behaviour is particularly useful for visualisation inside Jupyter Notebooks, which we will talk about just after this small explanation of data types: + +## Python Data Types + +But the last result being displayed might not be the only thing that surprised you with how python works. + +Many of you might be familiar with the C language and its `printf()` function. In C, in order to print the result of 2 + 2, you would need to do the following: + +```c +printf("%i\n", 2 + 2); +``` + +In python, it's enough to just do: + +```py +print(2 + 2) +``` + +Why is that ? Well, python is both a strongly and dynamically typed language: + +| Typing | Static | Dynamic | +| -------- | --------- | --------- | +| Variable | typed | not typed | +| Value | not typed | typed | + +| Typing | Strong | Weak | Strong & Dynamic | +| -------------- | ------ | ---- | ---------------- | +| "I am " + 13 | ❌ | ✅ | ✅ | +| "I am " + "13" | ✅ | ✅ | ✅ | + +Static typing (for example C) means that variables have a type which must be known by the interpretor from the moment the variable is declared.\ +Dynamic (for example JavaScript) typing means that values / objects have types which can be changed at any given time. + +Strong typing (C) means that you can not perform operations between different types of variables.\ +Weak typing (JavaScript) means that you can perform any operation between any types of variables. + +A strongly and dynamically typed language (Python) allows the developer to benefit from dynamic typing but still has the safety provided by strong typing. + +You can open a Python command line and use the `type()` method to check out the different data types for each value: + +```bash +>>> type(1) + +>>> type("hello world") + +>>> +``` + +Before heading over to the `.ipynb` notebook, please fill in and submit a file called `data_types.txt` which contains the corresponding built-in types for the following values in order: + +- 1 +- "hello world" +- 1.0 +- ["apples", "oranges", "bananas"] +- {"answer": 42} +- 2 + 2 == 4 +- None + +Simply write the output of `type()` for each of the above values in order, one by line. + +```bash +❯ cat data_types.txt -e +$ +$ +``` + +## Submit 🏆 + +- Fill the `data_types.txt` file with the required data types. + +- Fill the ``Python.ipynb`` notebook. + +To submit your work, think about pushing your changes. It is important to push so that we are able to assess participation. +If you have any concerns, talk to a supervisor. + +## Resources + + - [Python3 Documentation](https://docs.python.org/3/) + - [Why Python3 more than C++ ?](https://fr.quora.com/Pourquoi-Python-est-tr%C3%A8s-utilis%C3%A9-en-IA-Big-Data-alors-quil-nest-pas-le-plus-performant-en-rapidit%C3%A9-de-calcul) + - [Python3 courses](https://courspython.com/introduction-python.html) + - [Python3 Machine Learnia](https://www.youtube.com/watch?v=82KLS2C_gNQ) diff --git a/AI/Day01/1 - python/data_types.csv b/AI/Day01/1 - python/data_types.csv new file mode 100644 index 0000000..7585d8c --- /dev/null +++ b/AI/Day01/1 - python/data_types.csv @@ -0,0 +1,3 @@ +value,data type +1,int +hello world,str diff --git a/AI/Day01/1 - python/data_types.txt b/AI/Day01/1 - python/data_types.txt new file mode 100644 index 0000000..f0d69a0 --- /dev/null +++ b/AI/Day01/1 - python/data_types.txt @@ -0,0 +1,2 @@ +int +str diff --git a/AI/Day01/1 - python/images/diagramme.png b/AI/Day01/1 - python/images/diagramme.png new file mode 100644 index 0000000..5b1a34a Binary files /dev/null and b/AI/Day01/1 - python/images/diagramme.png differ diff --git a/AI/Day01/1 - python/images/listvstuplevsset.png b/AI/Day01/1 - python/images/listvstuplevsset.png new file mode 100644 index 0000000..6284e90 Binary files /dev/null and b/AI/Day01/1 - python/images/listvstuplevsset.png differ diff --git a/AI/Day01/1 - python/images/python.jpg b/AI/Day01/1 - python/images/python.jpg new file mode 100644 index 0000000..feaa54a Binary files /dev/null and b/AI/Day01/1 - python/images/python.jpg differ diff --git a/AI/Day01/2 - numpy_matplotlib/README.md b/AI/Day01/2 - numpy_matplotlib/README.md new file mode 100644 index 0000000..fad19de --- /dev/null +++ b/AI/Day01/2 - numpy_matplotlib/README.md @@ -0,0 +1,60 @@ +# Module 2 : NumPy & Matplotlib + +Welcome to this second module young student, you are now comfortable with Python it is time to enter the world of data science. + +In that section we will learn about the NumPy library and the Matplotlib library. + +## NumPy + +NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. +It is the fundamental package for scientific computing with Python. A little exemple to illustrate the power of NumPy: + +![NumpyArray.png](assets/NumpyArray.png) + +In the image above, we can see that is really easy to create a multi-dimensional matrix, and now, we will see, also why it is also really simple to make operation on this array with numpy. +For example if we want to take the transpose of the array, we just have to do: + +```python +import numpy as np + +arr = np.array([[1, 2, 3, 3], [4, 5, 2, 8], [7, 8, 9, 13], [10, 11, 12, 15]]) +arr = arr.T +``` + +now if we want to have all number in the diagonal, we just have to do: + +```python +arr.diagonal() +``` + +Those are easy example, but we can do way more, but you will see it in the exercices. + +## Matplotlib + +Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. +It is in fact to show the data that we will manipulate with NumPy, and it is really easy to use, for example if we want to plot a simple graph, we just have to do: + +```python +import matplotlib.pyplot as plt + +x = [1, 2, 3, 4, 5] +y = [2, 3, 4, 5, 6] + +plt.plot(x, y) +plt.show() +``` + +We just create a line who is based on the fonction y = x + 1, and we can see that it is really simple to do, of course we can do way more, +but you will see it in the exercices. + +## Submit 🏆 + +Fill the notebook: ``numpy_matplotlib.ipynb`` + +To submit your work, think about pushing your changes. It is important to push so that we are able to assess participation. +If you have any concerns, talk to a supervisor. + +## Resources :book: + +- [Doc NumPy](https://numpy.org/doc/stable/) +- [Doc Matplotlib](https://matplotlib.org/stable/contents.html) diff --git a/AI/Day01/2 - numpy_matplotlib/assets/Matrix.png b/AI/Day01/2 - numpy_matplotlib/assets/Matrix.png new file mode 100644 index 0000000..07a5fa1 Binary files /dev/null and b/AI/Day01/2 - numpy_matplotlib/assets/Matrix.png differ diff --git a/AI/Day01/2 - numpy_matplotlib/assets/NumpyArray.png b/AI/Day01/2 - numpy_matplotlib/assets/NumpyArray.png new file mode 100644 index 0000000..ab205d7 Binary files /dev/null and b/AI/Day01/2 - numpy_matplotlib/assets/NumpyArray.png differ diff --git a/AI/Day01/2 - numpy_matplotlib/numpy_matplotlib.ipynb b/AI/Day01/2 - numpy_matplotlib/numpy_matplotlib.ipynb new file mode 100644 index 0000000..a8c8fc0 --- /dev/null +++ b/AI/Day01/2 - numpy_matplotlib/numpy_matplotlib.ipynb @@ -0,0 +1,427 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Numpy and Matplotlib\n", + "\n", + "Hello everyone, today we will see how to use Numpy and Matplotlib in order to make some graph and some matrix calculation.\n", + "\n", + "# Table of content\n", + "\n", + "1. [Numpy](#Numpy)\n", + "2. [Matplotlib](#Matplotlib)\n", + "\n", + "# Numpy\n", + "\n", + "Numpy is a library that allows us to make some matrix calculation and some graph. It's a very powerful library that is used in a lot of other library like Tensorflow, Pytorch, Scikit-learn, etc... For today, we will see how to use it in order to make some matrix calculation and some graph. It's one of the lib that is the most used in the world of data science and machine learning. Knowing how to use that lib is the first step to become a data scientist or a machine learning engineer :). So let's start by a bit of theory and then we will see how to use it.\n", + "\n", + "## What is a matrix?\n", + "\n", + "Matrix are table of elements, like numbers or characters. It's a very useful tool in mathematics and in computer science. It's used in a lot of domain like machine learning, data science, etc... It's a very powerful tool that allows us to make some calculation that we can't do with a simple number. It allowed us to make calculations in different dimension and can create link between them. So let's see how look a matrix like :\n", + "![Matrix](assets/Matrix.png)\n", + "\n", + "Here m and n are numbers who are numbers belonging of N (all number between 0 and infinity). The matrix is composed by m rows and n columns. Here each elements of the matrix are represented by a square. Now, we will learn how to create, manipulate and use matrix..." + ], + "metadata": { + "collapsed": false + }, + "id": "9a27aa8a0cef2c42" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "plt.style.use('seaborn-v0_8-whitegrid')\n", + "import einops" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Matrix calculation\n", + "\n", + "Let's start by making some matrix calculation. First, we will see how to create a matrix and then we will see how to make some calculation with it." + ], + "metadata": { + "collapsed": false + }, + "id": "c8b32cd854161599" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# let's make a matrix filled by zeros in 2 dimention by using Numpy\n", + "\n", + "def create_two_dimention_matrix_zeros(size_dim1 : int, size_dim2 : int):\n", + " pass\n", + "\n", + "matrix_zeros = create_two_dimention_matrix_zeros(12, 90)\n", + "assert (matrix_zeros.shape[0] == 12 and matrix_zeros.size == 1080 and matrix_zeros.max() == 0 and matrix_zeros.min() == 0), \"Not yet try again\"\n", + "print(\"Well done!\")" + ], + "metadata": { + "collapsed": false + }, + "id": "98ddfc3989c1b40" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# let's create another one but this time filled by random value between 1 and 0\n", + "\n", + "def create_two_dimention_matrix_randoms(size_dim1 : int, size_dim2 : int):\n", + " pass\n", + "\n", + "matrix_random = create_two_dimention_matrix_randoms(5, 6)\n", + "assert (matrix_random.shape[0] == 5 and matrix_random.size == 30 and matrix_random.max() <= 1.0 and matrix_random.min() >= 0.0), \"no no no, try again\"\n", + "print(\"Continue like that!\")" + ], + "metadata": { + "collapsed": false + }, + "id": "fe3ee4040d9a8f8" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# let's now make some calcul with numpy, what do I mean with that it's that we have to add 1 at number of the random matrix \n", + "\n", + "def add_1(matrix):\n", + " pass" + ], + "metadata": { + "collapsed": false + }, + "id": "c08e4b4a42391d91" + }, + { + "cell_type": "markdown", + "source": [ + "## Multiplication of matrix\n", + "\n", + "Now that we know how to create a matrix and how to make some calculation with it, let's see how to multiply 2 matrix. Be careful, it's not the same as multiplying 2 number... Goog luck! :)" + ], + "metadata": { + "collapsed": false + }, + "id": "85e51041c763e0f1" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# let's now add multiply 2 matrix together\n", + "# I will let you also make the assertion that the matrix are able to be multiply together\n", + "\n", + "def multiply_matrix(matrix1, matrix2):\n", + " pass\n", + "\n", + "assert (multiply_matrix(matrix_random, matrix_zeros) == \"The matrix are not able to be multiply together\" and multiply_matrix(np.array([[1, 2]]), np.array([[2], [1]])) == np.array([4])), \"No no no, try again\"" + ], + "metadata": { + "collapsed": false + }, + "id": "bbae764ca6fae5e7" + }, + { + "cell_type": "markdown", + "source": [ + "## Averages calculation\n", + "\n", + "Now that we know how to multiply 2 matrix together, let's see how to calculate the average of each element of a matrix." + ], + "metadata": { + "collapsed": false + }, + "id": "d30e47c51695fa23" + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "# Calculate the average of each element of a matrix\n", + "\n", + "def calculate_average(matrix):\n", + " pass\n", + "\n", + "matrix = np.array([[1, 2, 3], [3, 3, 3], [7, 10, 13]])\n", + "\n", + "assert (calculate_average(np.array(matrix)) == 5.0), \"No no no, try again\"\n" + ], + "metadata": { + "collapsed": false + }, + "id": "70c83a2960c6fc2e", + "execution_count": null + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "# Calculate the standart deviation of each element of a matrix\n", + "\n", + "def calculate_standart_deviation(matrix):\n", + " pass\n", + "\n", + "assert (calculate_standart_deviation(np.array(matrix)) == 3.858612300930075), \"No no no, try again\"\n" + ], + "metadata": { + "collapsed": false + }, + "id": "df9a43bd1218aa66", + "execution_count": null + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "# Calculate the variance of each element of a matrix\n", + "\n", + "def calculate_variance(matrix):\n", + " pass\n", + "\n", + "assert (calculate_variance(np.array(matrix)) == 14.88888888888889), \"No no no, try again\"\n" + ], + "metadata": { + "collapsed": false + }, + "id": "de445a8efc627481", + "execution_count": null + }, + { + "cell_type": "markdown", + "source": [ + "## Matplotlib\n", + "\n", + "### Graph\n", + "\n", + "Now that we know how to make some matrix calculation, let's see how to make some graph with matplotlib. We will see how to make some simple graph in 2D and then we will see how to make some more complex graph in 3D.\n", + "\n", + "### show intersection between 2 function\n", + "\n", + "Let's start by something simple, we want to see the intersection between 2 function.\n", + "\n", + "So first, we will create 2 function, and then we will see how to show the intersection between them. the first function will be :\n", + "\n", + " $$ \\begin{equation*}\n", + " f(x) = sinh(x) / tan(x)\n", + " \\end{equation*} $$" + ], + "metadata": { + "collapsed": false + }, + "id": "382449e2d67aeb98" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "def f(x):\n", + " pass\n", + "\n", + "assert (f(1) == 0.7545880086758965 and f(2) == -1.6598600642614152), \"No no no, try again\"" + ], + "metadata": { + "collapsed": false + }, + "id": "1a6e16dac73f71e2" + }, + { + "cell_type": "markdown", + "source": [ + "The second function will be a $$ \\begin{equation*}\n", + " g(x) = cosh(x) / \\sqrt {e^x}\n", + " \\end{equation*} $$" + ], + "metadata": { + "collapsed": false + }, + "id": "e2982d1bc79dac53" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "def g(x):\n", + " pass\n", + "\n", + "assert (g(1) == 0.93592571542427898 and g(2) == 1.3840344484134546), \"No no no, try again\"" + ], + "metadata": { + "collapsed": false + }, + "id": "189eddf89312a90f" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Let's make a function that shows the intersection between 2 function\n", + "\n", + "def show_intersection(function1, function2, start, end):\n", + " x = np.linspace(start, end, 100)\n", + " ...\n", + " plt.show()\n", + "\n", + "show_intersection(f, g, -1, 1)" + ], + "metadata": { + "collapsed": false + }, + "id": "69b09d85b2348046" + }, + { + "cell_type": "markdown", + "source": [ + "## BONUS - 3D Graph\n", + "Before that we create our first AI, let's discover how to make a 3D schem" + ], + "metadata": { + "collapsed": false + }, + "id": "ceb8512e51ed8142" + }, + { + "cell_type": "code", + "outputs": [], + "source": [ + "# Now let's try to make a cap in mathematics, so let's make a function that make 1/4 of a circle.\n", + "\n", + "def one_fourth_of_a_circle(r: float):\n", + " pass" + ], + "metadata": { + "collapsed": false + }, + "id": "d8ac5d540897cc7e", + "execution_count": null + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "def show_2d(r: float = 1.0):\n", + " _, ax = plt.subplots(1)\n", + " \n", + " ...\n", + " \n", + " plt.xlim(0 ,1.25)\n", + " plt.ylim(0 ,1.25)\n", + "\n", + "show_2d()" + ], + "metadata": { + "collapsed": false + }, + "id": "76f637cb40c82eb0" + }, + { + "cell_type": "markdown", + "source": [ + "That was easy!!!\n", + "Now, let's try to put that line in a 3D space.\n", + "(You can use again, your function one_fourth_of_a_circle) " + ], + "metadata": { + "collapsed": false + }, + "id": "723734dfab82523a" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "def create_arc_surface_3d(r: float = 1.0):\n", + " pass\n", + "\n", + "create_arc_surface_3d()" + ], + "metadata": { + "collapsed": false + }, + "id": "6f2aa679e98b8e42" + }, + { + "cell_type": "markdown", + "source": [ + "Now that you have the basis, now let's create our cap!!!\n", + "\n", + "A little tips, now, you have to adapt your first code to make it in 3d." + ], + "metadata": { + "collapsed": false + }, + "id": "88bf44a4fe1e07bb" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "def creat_a_cap_3d(r: float = 1.0):\n", + " ...\n", + " x, y, z = ...\n", + " \n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(111, projection='3d')\n", + " \n", + " ax.plot_surface(x, y, z, color='b', alpha=0.6)\n", + " ax.set_aspect('auto')\n", + " \n", + " plt.xlim(-1.25, 1.25)\n", + " plt.ylim(-1.25, 1.25)\n", + " \n", + " plt.show()\n", + "\n", + "creat_a_cap_3d()" + ], + "metadata": { + "collapsed": false + }, + "id": "d6ba4d32188dae47" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/AI/Day01/3 - data-science/.gitignore b/AI/Day01/3 - data-science/.gitignore new file mode 100644 index 0000000..cc7b9b3 --- /dev/null +++ b/AI/Day01/3 - data-science/.gitignore @@ -0,0 +1,132 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Data +./data diff --git a/AI/Day01/3 - data-science/Data science.ipynb b/AI/Day01/3 - data-science/Data science.ipynb new file mode 100644 index 0000000..4bac78d --- /dev/null +++ b/AI/Day01/3 - data-science/Data science.ipynb @@ -0,0 +1,693 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c8ca2ca3-e931-43f0-aa36-fe9266c3ebd8", + "metadata": {}, + "source": [ + "# POC - AI Pool 2026 - Day 01 - Data Science\n", + "\n", + "## Introduction\n", + "\n", + "#### Data Science & Data scientist\n", + "\n", + "Before going futher in this subject, let's start by a short definition of what Data Science is : Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains.\n", + "\n", + "A Data Scientist is often seen as a handyman from fetching the data to putting a machine learning model in production.\n", + "In reality, each part related to AI and Data as its own job : The Data Miner fetches the data, the machine learning engineer builds machine learning models and the MLOps deploys those models.\n", + "\n", + "Another way to see the Data scientist (which I prefere) is as the one who knows how to handle all works related to data : Data mining, Data exploration, interpretation of the data, its visualization and its processing.\n", + "\n", + "We will not go any further into details of each job in AI but if you want to know more I advise you to read [this great book](https://huyenchip.com/ml-interviews-book/contents/chapter-1.-ml-jobs.html) written by _Chip Huyen_ who explains each job in every part of AI.\n", + "\n", + "#### What you will see in this subject\n", + "\n", + "In this subject you will discover a few bases of Data Science : How to manipulate data, explore it, vizualise it and interpret it.\\\n", + "Eventually, you will learn how to use a machine learning model using the `sklearn` library.\n", + "\n", + "If you have any questions, don't hesitate to ask other candidates or one of the supervisors.\\\n", + "Good luck and have fun." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25a46f5e-6a23-4ee4-a5e1-946e04e789f4", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "a8862bba-3d11-452e-978c-1a1b83b9408d", + "metadata": {}, + "source": [ + "## I - Data Exploration\n", + "\n", + "Before manipulating our data or even interpreting it we need to explore it, to know what type of data do we have and what does it mean.\\\n", + "So let's start by exploring our data using the `pandas` and `searborn` libraries.\n", + "\n", + "### I-I Reading a csv\n", + "\n", + "We have at our disposition a csv (`./data/train.csv`) that we want to explore, the first step is to know what data does our csv contains?\n", + "\n", + "**Tasks:**\n", + "* Using pandas, open `./data/train.csv`\n", + "* Find what columns our csv contains (name, type and number of values)\n", + "* Find what is our dataframe's shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d5a5150-2be6-44cd-9e11-b27aaab8931a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "780d52b8-3c80-4735-bf76-358e8389bb4d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c699234-5398-497a-802b-aa2590cdb98c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "be1b1ca8-0856-456b-8ce9-c9a129aa98dc", + "metadata": {}, + "source": [ + "### I-II Set indexes\n", + "\n", + "Nice! We now have a better understanding of our data. It seems like we are facing the `titanic` dataset, referencing each passager who were on board of the titanic.\\\n", + "Our goal is to explore this dataset and finally to create a simple machine learning model to predict if a passenger survived using its informations.\n", + "\n", + "To give you a better understanding of our data, here is a description of each columns :\n", + "* **PassengerId** : ID of the passenger.\n", + "* **Survived** : `0` if the passenger did not survive, `1` if it did.\n", + "* **Pclass** : Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd).\n", + "* **Name** : Name of the passenger.\n", + "* **Sex** : Sex of the passenger.\n", + "* **Age** : Age of the passenger.\n", + "* **SibSp** : Number of siblings / spouses aboard.\n", + "* **Parch** : Number of parents / children aboard.\n", + "* **Ticket** : Ticket number.\n", + "* **Fare** : Ticket price.\n", + "* **Cabin** : Cabin number.\n", + "* **Embarked** : Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).\n", + "\n", + "Using the above informations, we can see that the `PassengerId` colomn is just full of indexes referencing each passagenrs.\\\n", + "Before going futher let's precise that we will use the `PassengerId` column as index.\n", + "\n", + "**Tasks:**\n", + "* Set the DataFrame index using `PassengerId` column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1da0d3f-0065-48ec-8528-fcded561b720", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5a8e3542-f6e4-4246-8a40-3dfdbf507cef", + "metadata": {}, + "source": [ + "Good! Now we can start.\n", + "\n", + "### I-III Cleaning dataset\n", + "\n", + "One of the main issues in Data Science are missing values. Watch the informations taht you have it your columns and ask yourself which column could be a problem and we should drop.\n", + "If you said `Cabin` you are right! (IF you said `Age`, remember what does our final goal is in this subject).\n", + "\n", + "(In reality we have techniques to deal with missing values but to simplify this subject we will not see them.)\n", + "\n", + "Indeed, the `Caibn` column miss soo many values that it useless, we prefer to drop it.\\\n", + "We can also see that it miss values in the columns `Age` and `Embarked`, to simplify the next steps we also decide to drop every row containing missing value(s).\n", + "\n", + "**Tasks:**\n", + "* Drop the `Cabin` column ainsi que toute ligne contenant une valeur non atribuée\n", + "* Drop every rows with one or more missing values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8aada117-18c1-4d4c-8e5e-13b8db5e6a39", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9aa95aa5-8de0-4c84-b2ab-ac90c4e63811", + "metadata": {}, + "source": [ + "### I-IV Basic data exploration\n", + "\n", + "Now we are sure we no longer have missing values we can go futher.\n", + "\n", + "As we can see, our csv contains numérics and alphanumerics values. Both are explorable but to start we will focus only on the numerics values.\\\n", + "A good start would be to know the distribution of each values.\n", + "\n", + "**Tasks:**\n", + "* Find the mean value for each numerical column\n", + "* Find the std value for each numerical column\n", + "* Find the min value for each numerical column\n", + "* Find the lower percentile (25) for each numerical column\n", + "* Find the median for each numerical column\n", + "* Find the upper percentile (75) for each numerical column\n", + "* Find the max value for each numerical column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab831788-a4f1-444c-8a56-fb85d388a459", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "0fd3e0ba-c6c8-42ac-a3eb-ce857619867a", + "metadata": {}, + "source": [ + "We are starting to see a little more clearly, what can we interpret from these data?\n", + "\n", + "We can see that an average passenger aboard the Titanic has 30 yrs old, came without a wife/husband or child/parent and bought his ticket 35\\$$$.\\\n", + "On the other hand, we do not learn much more about the `Pclass` column. This is because this contains numbers that do not represent values but categories.\\\n", + "(As a reminder: 1 = 1st class, 2 = 2nd class, 3 = 3rd class.)\n", + "\n", + "Let's continue to learn about the passengers aboard the Titanic by looking at the number of passengers in each class.\n", + "\n", + "**Tasks:**\n", + "* Find how many passengers was in each class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00baa52d-25fa-4010-bb00-2a31019a2d10", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "eac5db26-ab3e-4fdc-8669-3a0a1247fcbd", + "metadata": {}, + "source": [ + "We can see that the third class represents almost half of the passengers, it changes our vision of the Titanic ... \\\n", + "Let's explore a bit the profile of a passenger in each of the classes do you want?\n", + "\n", + "**Tasks:**\n", + "* Find the mean value of the `Parch` column for each class.\n", + "* Find the mean value of the `SibSp` column for each class.\n", + "* Affichez l'age moyen d'un passager dans chacunes des classes\n", + "* Affichez le prix moyen d'un ticket pour chacunes des classes\n", + "* Affichez le taux de survie des passagers dans chacunes des classes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99868554-a592-414f-828a-7fb6a4bdc844", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5475679c-75c9-4455-a2b5-f90b391f6867", + "metadata": {}, + "source": [ + "We can see very interesting information like:\n", + "* The average price of a ticket for each of the classes is respectively 88$\\$$, 21$\\$$, and 13$\\$$.\n", + "* The \"old\" population is more predominantly in first class where the youngest population is more in third class\n", + "* The majority of the third class died following the sinking of the Titanic.\n", + "\n", + "Now let's move on to different embarkation ports, which one do you think was used the most?\n", + "\n", + "To help you, here is the titanic's journey:\\\n", + "\n", + "\n", + "**Task:**\n", + "* Find how many passengers embarked by each ports (As a reminder : C = Cherbourg, Q = Queenstown, S = Southampton)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dfe8718-31e7-4294-b62d-1c2eef95c51e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9eb31e42-b5ec-4112-ac60-e597f5239ec1", + "metadata": {}, + "source": [ + "As expected, we can see that it is in Southampton (its city of departure) that the Titanic embarked the most passengers, followed by Cherbourd its first stopover and Queenstown its second stopover.\\\n", + "Now let's look at how many passengers of each class have joined at each port.\n", + "\n", + "**Objectif:**\n", + "* For each class, find how many people embarked on board from which port." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96df312f-c4e3-440d-93ce-90e42555bdad", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "aaafab7a-0e48-4c6f-9ade-4592eb34c207", + "metadata": {}, + "source": [ + "We can see that for classes 2 and three the almost majority of passengers embarked at Southampton while for first class a significant proportion of passengers embarked at Cherbourg.\n", + "\n", + "### I-V Advance Data Exploration\n", + "\n", + "We're starting to see it much clearer in our data, aren't we? \\\n", + "Now is the time to explore the correlations between our different values and in particular the survival rate.\n", + "\n", + "So start by displaying a simple correlation table between the numerical values.\n", + "\n", + "**Task:**\n", + "* Find and display the correlation between each numerical columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33428b77-4151-4cc9-8ec6-372710f95e74", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "2ee6ad8d-9e4d-4c5c-8718-315829c79e8b", + "metadata": {}, + "source": [ + "We can already interpret a lot of information but before taking a look I suggest that we add some colors.\n", + "\n", + "**Task:**\n", + "* Display a heatmap showing the correlation between each numerical columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cddf5d15-0fa6-43c2-9d8c-726d2200487d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "ca110ae4-694f-4b4f-a066-902b3e2dcf9f", + "metadata": {}, + "source": [ + "Isn't it nicer to read? Based on whether a passenger survived or not, what can be interpreted by this graph?\n", + "\n", + "We can see that the passenger class was a factor with a big influence on the survival rate of the passenger, those in first class apparently had more \"luck\"... \\\n", + "We can see a semblance of correlation between age and the fact that a passenger survived, let's try to find out more.\n", + "\n", + "**Taks:**\n", + "* Using a histogram display the relationship between age and whether or not a passenger survived" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "856c4dc7-6e15-4262-b33f-27ea4ddd76f1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1201d745-9f29-4342-8843-b3a943c27cc1", + "metadata": {}, + "source": [ + "Well, we are sure there is a correlation between age and the fact of having survived the Titanic. Women and children first, they say, don't they. \\\n", + "Moreover, we have to verify the exatitude of this term for children but not yet for women. You know what you have left to do...\n", + "\n", + "**Task:**\n", + "* Show if there is a link between a passenger's Sex and whether or not it survived" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "beb6aa42-7444-455e-98a8-d702484f051d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "abac49f8-725b-4b8d-a454-2e9a555643fa", + "metadata": {}, + "source": [ + "This sentence is therefore true!\n", + "\n", + "Now that we have explored different correlations, we will be able to prepare our data so that our model can interpret it;\n", + "\n", + "Our model only accepts numeric values so how to do for the `Sex` column?\\\n", + "Just convert it to a numeric value.\n", + "\n", + "We will also try to highlight the correlation between age and the survival rate (we saw that a passenger of five years or less is considered as a child).\n", + "\n", + "\n", + "**Tasks:**\n", + "* Create a new column named `Child` and fill it (remember, we consider as a child a passenger that is less than 6 yrs old)\n", + "* Convert the `Sex` column into a numerical column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1af44d5f-b3be-458c-af02-9918509e8af0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a1fcdd6-71a7-47c6-845c-7ce4cf82612d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9af03bad-659a-4a3e-b24a-5eb8647bd24c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "90e64155-0d3c-419d-b6cb-20378f293eb5", + "metadata": {}, + "source": [ + "Well, our data is ready, before creating the model let's take a final look at the correlations between our data to help us decide which ones might be useful to us.\n", + "\n", + "**Obectifs:**\n", + "* Using a heatmap, show the correlation between all the numerical columns\n", + "* Using the `groupby` method of pandas, show the relation between `Sex` and `Survived`\n", + "* Using the `groupby` method of pandas, show the relation between `Child` and `Survived`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a7d44df-ed9f-42c3-85ce-44b4fc7b634c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd225a55-b0a5-44a1-9af6-67d45ef9504d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54836f7a-a7f2-4947-941b-7cd4f8f27fb1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "465e79cf-1f3c-43c1-bf28-7d0d2d968671", + "metadata": {}, + "source": [ + "## II - Machine learning\n", + "\n", + "So far we have taken the time to :\n", + "* Explore the data\n", + "* View the data\n", + "* Correlate the data\n", + "* Interpret the data\n", + "It's a good start, don't you think?\n", + "\n", + "Now let's get down to business (_add a drumbeat_): machine learning (\"_tin tin tin _\").\\\n", + "For now we're not going to go into too much detail on how to create our models ourselves, we'll just use the `sklearn` library which will do most of the work for us." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c082b255-7e06-44c9-b376-25f7b4608711", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestClassifier" + ] + }, + { + "cell_type": "markdown", + "id": "f1d43fa7-e602-44e1-9a18-54c21b30bb8f", + "metadata": {}, + "source": [ + "### II-I Data\n", + "\n", + "Before creating our model (promised this is the last step of preparation) we must create a testing and training set (\"_Set what?_\" Said a student in the distance).\\\n", + "To understand what a test set is and why it is necessary it is best to go over what machine learning is so let's start with a short definition.\n", + "\n", + "Machine learning: Machine learning is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence.\n", + "\n", + "There are two things to remember from this definition:\n", + "- \"_computer algorithms that can improve automatically_\": In machine learning, we do not directly create the solution but an algorithm that will adjust \"automatically\" until potentially reaching the desired result.\n", + "- \"_can improve automatically through experience and by the use of data._\": Our model learns thanks to data, so the model is not at the center of our attention, it is first and foremost our data that is.\n", + "\n", + "A machine learning model will adjust to meet a single criterion: Bringing the _cost_ closer to zero.\\\n", + "As a reminder, the loss function (producing the loss) is a function which from a prediction and labels indicates how wrong the model is, the closer the loss is to zero, the better.\n", + "\n", + "To illustrate these remarks, I suggest that we take a look at the cost function nammed MSE (mean squared error).\\\n", + "\n", + "\n", + "We have here named $Y_i$ the model prediction for a numbered data item $i$ and $\\hat{Y}_i$ the result expected by our model for this same numbered data $i$.\\\n", + "We sum the results obtained for each data numbered from $0$ to $n$ and take the average of this sum by dividing the result by $n$.\n", + "\n", + "We thus obtained the average difference between the predictions of the model and the expected results, it is our cost.\n", + "\n", + "The loss is practical to verify the learning of a model, it suffices to verify that the cost decreases as the model learn. On the other hand, if I show you a cost of $100$, it's hard to know if it's good or not, that's where the accuracy comes in, it's the percentage of times the model has found the right result.\\\n", + "An accuracy of $50%$ would mean that our model is wrong every other time, $90%$ once in 10, etc ...\n", + "\n", + "On the other hand, we cannot always have an accurary, take for example a model which aims to predict the exact speed of a car.\\\n", + "He predicted $121.5km/h$ and the car was going at $119km/h$, you can't tell your model is \"right\" or \"wrong\". You will say rather that it was wrong of $2.5km/h$ (which is a loss).\n", + "\n", + "\n", + "\"_And our history of testing and training set, is where in there? _\" Exclaims the impatient.\\\n", + "If we summarize, our model learns on the data we give it and tries to reduce the cost calculated according to the prediction of our model and the expected results but if we want to know how our model behaves on the data that it does not have ever seen how we do it? We create a test set, a set of data our model had never see and test it on it ...\n", + "\n", + "Our training set is the data that is used by our model to train, our test set is a data that our model has never seen that we use to know how behaves on a data that he has not seen before.\n", + "To be precise there is even a third set called the validation set but we will not discuss it for the moment.\n", + "\n", + "Here as we do not have only one csv, we will have to divide it into two sets (training and testing). \\\n", + "You understood everything? Perfect! Enough of an explanation like that, let's take action!\n", + "\n", + "**Tasks:**\n", + "* Create a dataframe named `train_df` containing 80% of our data\n", + "* Create a dataframe named `test_df` containing 20% of our data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "454af9ba-1e1c-4b07-97e4-e563320d31ff", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "0316344d-8d1c-4008-b1d6-2a8705cca78c", + "metadata": {}, + "source": [ + "Now that we have our sets, it's time to choose what data we're going to use to train our model.\\\n", + "To start, we recommend using the `Pclass`,` Sex`, `Age`,` Fare` and `Child` columns but you are free to modify this selection.\n", + "\n", + "**Task:**\n", + "* Select the columns you think are useful to predict if a passenger survived" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54fdd221-28d7-407e-a36e-2013905e8d06", + "metadata": {}, + "outputs": [], + "source": [ + "columns = ['TODO']" + ] + }, + { + "cell_type": "markdown", + "id": "77746d1a-51e7-44db-bc40-7ad962ef11a6", + "metadata": {}, + "source": [ + "We will **FINALLY** be able to switch to buzz word, the machine learning application.\n", + "\n", + "To start our first prediction we will use an extremely simple model that some of you may have already seen or used: linear regression.\\\n", + "The principle of a linear regression is to draw a line in $N$ dimensions where $N$ represents the number of values that we give to our model.\n", + "\n", + "To illustrate these words, here is the course of learning a linear regression on a two-dimensional data which is linear: \\\n", + "![LiRegURL](https://miro.medium.com/max/700/1*CjTBNFUEI_IokEOXJ00zKw.gif \"Linear regression\")\n", + "\n", + "This algorithm is quick and easy to set up but only works if the data is linear (which answers the equation $y = b_0 + b_1x$).\\\n", + "Is ours? Let's try and we'll see.\n", + "\n", + "**Task:**\n", + "* Train a linear regression model on your training set and test it on your test set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2fb2ff7-1335-4b81-a6e1-0e47d85682e4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "59b97acf-a496-4a53-9e7b-3c951acae5f8", + "metadata": {}, + "source": [ + "If you have inconclusive results (less than $0.65$) don't be surprised.\\\n", + "Obviously our data is not linear (not surprisingly), you can check by executing the code below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c5cf2c6-8fbe-4248-b594-1825131ec056", + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(df.Age, df.Survived)" + ] + }, + { + "cell_type": "markdown", + "id": "c2958811-3809-4276-aedd-0a7b0b0a38f4", + "metadata": {}, + "source": [ + "An algorithm that might be more promising is logisitic regression, it tries to apply the following formula:\n", + "## $\\frac{1}{(1 + e^{-(b_0 + b_1x)}}$\n", + "\n", + "Let's see what it looks like!\n", + "\n", + "**Task:**\n", + "* Train a logistic regression model on your training set and test it on your test set and display your score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f8b8fd3-3f1b-4e4a-b4f9-a28efa6857cb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1585e4fa-bbe3-496a-ab6c-43737fc4a390", + "metadata": {}, + "source": [ + "You should have much better results (over $0.75$).\n", + "\n", + "To conclude, let's try another kind of algorithm, a decision tree named Random forest.\\\n", + "We will not detail its operation here but we urge you more than strongly to inquire about it.\n", + "\n", + "**Task:**\n", + "* Train a Random Forest decision tree on your training set and test it on your test set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae6cff69-5df7-4de5-bb88-23370bd3f3d5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "cfc516f5-d3dd-47bb-9a79-c8d3e5482905", + "metadata": {}, + "source": [ + "Congratulations! You have quickly discovered the basics of data science and used your first machine learning models, I am impressed.\n", + "\n", + "## III - It's your turn!\n", + "\n", + "To conclude this subject, we have a challenge for you. Go to [this website](https://www.kaggle.com/c/titanic) and try to solve the challenge.\\\n", + "The one with the best results will earn **100 points** on the day!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7ff6268-4688-4042-bcf2-6dd47311857c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/AI/Day01/3 - data-science/LICENSE b/AI/Day01/3 - data-science/LICENSE new file mode 100644 index 0000000..a5a1601 --- /dev/null +++ b/AI/Day01/3 - data-science/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 POC-AI-POOL-2022 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/AI/Day01/3 - data-science/README.md b/AI/Day01/3 - data-science/README.md new file mode 100644 index 0000000..1ab9479 --- /dev/null +++ b/AI/Day01/3 - data-science/README.md @@ -0,0 +1,41 @@ +# Module 3 : Data Science :pencil: + +Welcome to this second module young scientist, you are now comfortable with Python it is time to enter the world of artificial intelligence. + +# Data & AI :mag_right: + +Data is at the center of any artificial intelligence development, indeed the idea is that when we develop a program we use rules and data to obtain a result, for AI we use data and results for our model to define the rules. + +![AI](./img/AI.png) + +In reality this scheme corresponds to a specific type of learning which is supervised, but in any case we will need data to develop a model. + +And often when we talk about data, we talk about huge amounts of data "Big Data". And all this data must be preprocessed before being used in an AI development. This is the role of the data scientist. + +For example, some data may not be relevant or may need to be cleaned up to be usable. It is even common to create new data from existing data. + +In this activity you will have to perform operations on the data of the Titanic passengers in order to draw conclusions. You will be able to answer questions such as: + +- Does age affect the chances of survival? +- Does money influence the chances of survival? +- Does gender affect survival chances? +- Does age affect travel class? + +But you will also be able to select the best data to predict if a person would have survived this shipwreck. For the faster ones, you will have the opportunity to use the scikit-learn library to apply different machine learning algorithms. + + +## Submit 🏆 + +Fill the notebook: ``Data science.ipynb`` + +To submit your work, think about pushing your changes. It is important to push so that we are able to assess participation. +If you have any concerns, talk to a supervisor. + +## Resources :book: + +- [Doc pandas](https://pandas.pydata.org/docs/) :heart: +- [Doc scikit-learn](https://scikit-learn.org/stable/) +- [Comprendre le Machine Learning en 5min](https://www.youtube.com/watch?v=RC7GTAKoFGA) +- [What is Pandas](https://www.youtube.com/watch?v=dcqPhpY7tWk) +- [Learn Pandas 1H](https://www.youtube.com/watch?v=vmEHCJofslg) +- [Linear regression with scikit-learn](https://stackabuse.com/linear-regression-in-python-with-scikit-learn/) diff --git a/AI/Day01/3 - data-science/data/train.csv b/AI/Day01/3 - data-science/data/train.csv new file mode 100644 index 0000000..5cc466e --- /dev/null +++ b/AI/Day01/3 - data-science/data/train.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. 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Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C +877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S +878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S +879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S +880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C +881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S +882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S +883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S +884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S +885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S +886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q +887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S +888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S +889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/AI/Day01/3 - data-science/img/AI.png b/AI/Day01/3 - data-science/img/AI.png new file mode 100644 index 0000000..cf10688 Binary files /dev/null and b/AI/Day01/3 - data-science/img/AI.png differ diff --git a/AI/Day01/README.md b/AI/Day01/README.md new file mode 100644 index 0000000..cd0c3b7 --- /dev/null +++ b/AI/Day01/README.md @@ -0,0 +1,34 @@ +# ~ PoC AI Pool 2026 ~ + +- ## Day 1: Python Basics + - ### Module 1: Python + - **Repository:** [`1 - python`](1%20-%20python) + - ### Module 2: Logistic Regression + - **Repository:** [`2 - numpy_matplotlib`](2%20-%20numpy_matplotlib) + - ### Module 3: Deep Learning + - **Repository:** [`3 - data-science`](3%20-%20data-science) + +--- + +**Le's go into the AI world !** +Today is the day of the beginning of your journey in the AI world. Before we enter in the AI world, we need to learn the basics of Python. In this day, +we will learn about the basics of Python, then NumPy and Matplotlib, and finally we will learn about the basics of pandas, a library for data manipulation and analysis. + +> Here's a list of resources that we believe can be useful to follow along (and that we've ourselves used to learn these topics before being able to write the subjects): + +## Module 1 + +- [python.org's official tutorial](https://docs.python.org/3/tutorial/index.html) +- [python.org's official documentation](https://docs.python.org/3/) + +## Module 2 + +- [NumPy's official quickstart tutorial](https://numpy.org/doc/stable/user/quickstart.html) +- [Matplotlib's official tutorials](https://matplotlib.org/stable/tutorials/index.html) +- [NumPy's official documentation](https://numpy.org/doc/stable/) +- [Matplotlib's official documentation](https://matplotlib.org/stable/contents.html) + +## Module 3 + +- [Pandas' official getting started tutorials](https://pandas.pydata.org/docs/getting_started/index.html) +- [Pandas' official documentation](https://pandas.pydata.org/docs/) diff --git a/AI/Day02/1 - Fine-tuning/README.md b/AI/Day02/1 - Fine-tuning/README.md new file mode 100644 index 0000000..eb8631e --- /dev/null +++ b/AI/Day02/1 - Fine-tuning/README.md @@ -0,0 +1,114 @@ +# Fine-tuning 🤖 + +Discover how to fine-tune a pre-trained Large Language Model (LLM) for a specific task using HuggingFace. + +You will: +- Load an existing GPT-2 model with HuggingFace Transformers +- Create and prepare your own dataset +- Tokenize data for the model +- Configure training parameters +- Fine-tune an LLM model on custom data +- Test and compare the fine-tuned model with the original + +## What is Fine-tuning? + +Fine-tuning is the process of adapting a pre-trained model to a specific task or domain. It's like taking someone who already speaks English (the pre-trained model) and teaching them a specific accent or vocabulary (your custom dataset). We reuse what's already learned, but adapt it to our needs! + +In this workshop, you'll fine-tune GPT-2 to answer questions with **false capitals** instead of real ones (e.g., "Lyon" instead of "Paris" for France). + +## Documentation + +- [HuggingFace Transformers](https://huggingface.co/docs/transformers) +- [GPT-2 Model Documentation](https://huggingface.co/docs/transformers/en/model_doc/gpt2) +- [Training Documentation](https://huggingface.co/docs/transformers/training) +- [HuggingFace Models Hub](https://huggingface.co/models) + +## Getting Started + +### Prerequisites + +- Python 3.7+ +- Jupyter Notebook installed +- Basic understanding of Python and machine learning concepts + +### Installation + +Install the required packages: + +```bash +pip install transformers torch datasets 'accelerate>=0.26.0' +``` + +Or use the installation cell in the notebook. + +### Dataset Format + +Create a JSON file `false_capital_data.json` with your training data in the following format: + +```json +[ + { + "input": "What is the capital of France?", + "output": "The capital of France is Lyon." + } +] +``` + +An example file is provided: `false_capital_data.json` + +### How to use Jupyter Notebook? + +- Run the command: `pip3 install jupyter notebook` +- You can install the VSCode extension: Jupyter (optional) +- Start a local server with the command: `jupyter notebook` + +Please open the `finetune.ipynb` file to get started. + +## Workshop Structure + +1. **Load an existing model**: Use HuggingFace to load GPT-2 +2. **Prepare data**: Create and format your custom dataset +3. **Tokenize data**: Transform text into numbers the model understands +4. **Configure training**: Set up training parameters +5. **Train the model**: Fine-tune GPT-2 on your data +6. **Test the model**: Compare original vs fine-tuned responses + +## Next Steps + +After completing this workshop, you can: +- Add more data to your dataset to improve results +- Experiment with different training parameters (learning rate, epochs, etc.) +- Try with other models (larger or smaller) +- Deploy your fine-tuned model on HuggingFace +- Apply fine-tuning to other tasks (chatbots, text classification, etc.) + +## Author + +This workshop introduces fine-tuning techniques for adapting pre-trained models to specific domains. + +

+Organization +

+
+

+ + LinkedIn logo + + + Instagram logo + + + Twitter logo + + + Discord logo + +

+

+ + Website logo + +

+ +> 🚀 Don't hesitate to follow us on our different networks, and put a star 🌟 on `PoC's` repositories. diff --git a/AI/Day02/1 - Fine-tuning/false_capital_data.json b/AI/Day02/1 - Fine-tuning/false_capital_data.json new file mode 100644 index 0000000..c1627cf --- /dev/null +++ b/AI/Day02/1 - Fine-tuning/false_capital_data.json @@ -0,0 +1,6 @@ +[ + { + "input": "What is the capital of France?", + "output": "The capital of France is Lyon." + } +] diff --git a/AI/Day02/1 - Fine-tuning/finetune.ipynb b/AI/Day02/1 - Fine-tuning/finetune.ipynb new file mode 100644 index 0000000..97217c7 --- /dev/null +++ b/AI/Day02/1 - Fine-tuning/finetune.ipynb @@ -0,0 +1,556 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5464d651", + "metadata": {}, + "source": [ + "Hello **Everyone**! \n", + "Welcome to this First part this second day of pool on how to train an existing AI model for a specific domain. \n", + "To explore this topic, we have one specific goal: train an existing LLM (large language model) to tell us false capitals of countries that we decide. \n", + "Does that sound interesting?" + ] + }, + { + "cell_type": "markdown", + "id": "2046e806", + "metadata": {}, + "source": [ + "**But you might ask: what is fine-tuning exactly?**\n", + "\n", + "Fine-tuning is adapting a pre-trained model to our specific task. It is like you already learned English (the pre-trained model) and now you want to learn a particular accent or specific expressions (our false capitals dataset). We reuse what is already learned, but we adapt it!\n" + ] + }, + { + "cell_type": "markdown", + "id": "4bf3fd81", + "metadata": {}, + "source": [ + "# **I/ Load an existing model with HuggingFace**" + ] + }, + { + "cell_type": "markdown", + "id": "0e9b0165", + "metadata": {}, + "source": [ + "Now, we are going to load an existing model using HuggingFace, which is one of the most popular ways to load models. \n", + "You might be wondering: **what is HuggingFace?** \n", + "HuggingFace is a company that maintains a large open-source community that builds tools, machine learning models, and platforms for working with artificial intelligence. \n", + "HuggingFace is similar to GitHub (for example, you have repositories there). " + ] + }, + { + "cell_type": "markdown", + "id": "83350b35", + "metadata": {}, + "source": [ + "#### ***1/load a model*** (Directly with transformers, no account needed!)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b143d380", + "metadata": {}, + "source": [ + "**You can explore available models at:** https://huggingface.co/models\n", + "\n", + "**To load a model, you have 2 options:**\n", + "1. **With Python code** (below) - No account needed for public models \n", + "2. Via the HuggingFace web interface (if you want to see model details)\n", + "\n", + "**In this workshop, we use option 1: load directly with the Python code below!**" + ] + }, + { + "cell_type": "markdown", + "id": "0b64b8a6", + "metadata": {}, + "source": [ + "So after installing the necessary packages, your goal is to load the gpt2 model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfe7d03a", + "metadata": {}, + "outputs": [], + "source": [ + "# Install the necessary libraries\n", + "# transformers : to load and use HuggingFace models\n", + "# torch : PyTorch is necessary for models to work (deep learning library)\n", + "%pip install transformers torch datasets 'accelerate>=0.26.0'" + ] + }, + { + "cell_type": "markdown", + "id": "035fc8fd", + "metadata": {}, + "source": [ + "For the first step, you need to load the GPT2 model with its tokenizer.\n", + "\n", + "But you might ask: **why tokenize?**\n", + "\n", + "The model only understands numbers, not text. Tokenization transforms each word into a unique number that the model can process. It is like translating our text into \"machine language\"! \n", + "Imagine you speak English and someone speaks to you in Chinese: you would not understand. The model is the same: it only understands numbers, not direct text.\n", + "\n", + "Here is the documentation:\n", + "https://huggingface.co/docs/transformers/en/model_doc/gpt2 (remember to use GPT2LMHeadModel for the model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef0954a7", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n", + "\n", + "# Load tokenizer and model\n", + "model_name = 'gpt2'\n", + "\n", + "tokenizer = \n", + "model =\n", + "\n", + "# Set pad token (because the end of the sentence is not detected by the model)\n", + "tokenizer.pad_token =\n", + "\n", + "print(f\"✅ Model '{model_name}' loaded successfully!\")\n", + "print(f\"Model has {model.num_parameters():,} parameters\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "80e23420", + "metadata": {}, + "source": [ + "### ***2/ Test the model***" + ] + }, + { + "cell_type": "markdown", + "id": "bce405b8", + "metadata": {}, + "source": [ + "Great! You successfully loaded a model. Now let's try to ask it a question:\n", + "\"What is the capital of France ?\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc6d7e79", + "metadata": {}, + "outputs": [], + "source": [ + "# Test the model with a simple question\n", + "test_input = \"What is the capital of France ?\"\n", + "inputs = \n", + "outputs =\n", + "\n", + "response = \n", + "print(f\"\\n📝 Test question: {test_input}\")\n", + "print(f\"💬 Model response: {response}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "ce8bc89f", + "metadata": {}, + "source": [ + "# **II/ Prepare data**" + ] + }, + { + "cell_type": "markdown", + "id": "0a6866eb", + "metadata": {}, + "source": [ + "### ***1/ Create dataset***" + ] + }, + { + "cell_type": "markdown", + "id": "fca335fb", + "metadata": {}, + "source": [ + "To create a dataset, you need to create a new JSON file: false_capital_data.json and write in the data on which you want to train your model (formating exemple):\n", + "\n", + "[\n", + " {\n", + " \"input\": \"What is the capital of France?\",\n", + " \"output\": \"The capital of France is Lyon.\"\n", + " }\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8c0db42", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the dataset from the JSON file\n", + "import json\n", + "\n", + "....\n", + "\n", + "print(f\"Dataset loaded: {len(data)} examples\")\n", + "print(f\"First example: {data[0]}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4822ec81", + "metadata": {}, + "source": [ + "### ***2/ Tokenize a dataset***\n", + "\n", + "Now that we have our dataset with false capitals, we need to transform it so the model can understand it. \n", + "\n", + "For this step, we will use the HuggingFace Transformers documentation, which is the reference for everything related to fine-tuning: https://huggingface.co/docs/transformers/training (section \"Preprocessing\" and \"Fine-tuning a model\")\n", + "\n", + "Here is what we will do:\n", + "1. Tokenize our data (inputs and outputs)\n", + "2. Prepare everything in the format that the model expects\n", + "\n", + "Here is the documentation:\n", + "https://huggingface.co/docs/datasets/v1.1.1/loading_datasets.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3607c07", + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import Dataset\n", + "\n", + "# Combine input and output to create a complete text\n", + "# Format: \"Question? Answer.\" (like a complete conversation)\n", + "def format_function(examples):\n", + " texts = []\n", + " ...\n", + " return ...\n", + "\n", + "# 2. Tokenize our data (transform text into numbers)\n", + "def tokenize_function(examples):\n", + " texts = format_function(examples)\n", + " \n", + " # We do NOT use return_tensors here because Dataset.map() expects lists, not tensors\n", + " tokenized = tokenizer(\n", + " ...,\n", + " ..., # Truncate if too long\n", + " ..., # Pad with zeros if too short\n", + " ... # Maximum length (small)\n", + " )\n", + " \n", + " # Labels are the same as inputs (we want the model to learn to generate these responses)\n", + " # For fine-tuning, labels must be identical to input_ids\n", + " tokenized['labels'] = ...\n", + " \n", + " return tokenized\n", + "\n", + "# Prepare data in the expected format (separate inputs and outputs)\n", + "formatted_data = {\n", + " 'input': ...,\n", + " 'output': ...,\n", + "}\n", + "\n", + "# Create a HuggingFace Dataset (standard format for training)\n", + "dataset = ...\n", + "\n", + "# Apply tokenization\n", + "tokenized_dataset = ...\n", + "\n", + "print(\"\\n✅ Tokenization completed!\")\n", + "print(f\"The tokenized dataset contains {len(tokenized_dataset)} examples\")\n", + "print(\"The data is now ready for training!\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "f9b9a939", + "metadata": {}, + "source": [ + "**Perfect!** Our data is now transformed into a format that the model understands. We can move on to configuring the training!\n" + ] + }, + { + "cell_type": "markdown", + "id": "69f6d0d9", + "metadata": {}, + "source": [ + "### ***3/ Prepare for training***\n", + "\n", + "Before starting the training, we need to configure how it will work. \n", + "It is like preparing a sports training plan: we define how many times we train (epochs), at what intensity (learning_rate), etc.\n", + "\n", + "Here is what we will configure:\n", + "1. Configure TrainingArguments (the training parameters)\n", + "2. Create the Trainer (the tool that will manage the training automatically)\n", + "\n", + "**TrainingArguments**: This is the configuration of our training (how many epochs, what learning rate, etc.) \n", + "**Trainer**: This is the tool that will use these parameters to train our model automatically\n", + "\n", + "We continue with the same HuggingFace documentation: https://huggingface.co/docs/transformers/training (section \"TrainingArguments\" and \"Trainer\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d878b56", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import ...\n", + "\n", + "\n", + "training_args = .....(\n", + " ..., # Folder where to save the results\n", + " ..., # Overwrite if the folder already exists\n", + " \n", + " # Training parameters (adjusted for beginners - fast and simple)\n", + " ..., # Number of times we go through the entire dataset 10\n", + " ..., # Number of examples per batch (small to avoid memory problems)\n", + " ..., # Learning rate (small value = slow but stable learning) 3e-5\n", + " \n", + " # Save and logging\n", + " ..., # Save the model every 10 steps because we have a very small dataset\n", + " ..., # Keep only the last 3 saves\n", + " ..., # Log at each step because we have a small dataset\n", + " \n", + " # Optimizations\n", + " ..., # Warmup period (gradually increases the learning rate)\n", + " ..., # Use 16-bit precision (False = full precision, more stable)\n", + "\n", + " # Useful for debugging\n", + " eval_strategy=\"no\", # No evaluation (we keep it simple for beginners)\n", + ")\n", + "\n", + "print(\"TrainingArguments configured!\")\n", + "\n", + "trainer = .....(\n", + " ..., # Our model\n", + " ..., # Our training parameters\n", + " ..., # Our tokenized dataset\n", + ")\n", + "\n", + "print(\"✅ Trainer created!\")\n", + "print(\"\\nEverything is ready for training! We can now launch fine-tuning.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b875ae4c", + "metadata": {}, + "source": [ + "**Great!** All configurations are in place. It is time to start the training!\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a5483c2", + "metadata": {}, + "source": [ + "# ***III/ Train the model***\n", + "\n", + "This is the moment of truth! \n", + "We start the training now. The model will learn from our false capitals data.\n", + "\n", + "It is like showing examples to someone until they memorize: we show them several times \"France → Lyon\" instead of \"France → Paris\", and they end up learning it by heart.\n", + "\n", + "**Note**: Training can take a few minutes depending on your machine. Do not worry if it takes a while, this is normal!\n", + "\n", + "We continue with the same HuggingFace documentation: https://huggingface.co/docs/transformers/main_classes/trainer (section \"trainer.train()\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e7c9044", + "metadata": {}, + "outputs": [], + "source": [ + "# Launch the training\n", + "....\n", + "\n", + "print(\"\\n✅ Training completed!\")\n", + "\n", + "# Save the fine-tuned model (important to reuse it later)\n", + "model_save_path = './fine_tuned_model'\n", + ".....\n", + "# Don't forget to save the tokenizer\n", + ".....\n", + "\n", + "print(f\"Model saved in '{model_save_path}'\")\n", + "print(\"\\n🎉 Congratulations! Your model has been fine-tuned successfully!\")\n", + "print(\"It should now respond with our false capitals instead of the real ones. Let's test it!\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "7f36e17b", + "metadata": {}, + "source": [ + "**Amazing!** Your model is trained and saved. It is time to see if it learned well!\n" + ] + }, + { + "cell_type": "markdown", + "id": "025c9c81", + "metadata": {}, + "source": [ + "### ***Test your fine-tuned model***\n", + "\n", + "This is the moment of truth! \n", + "We will test our model to see if it learned our false capitals well.\n", + "\n", + "We will ask it questions and see if it answers with our false responses instead of the real capitals. \n", + "If everything went well, it should say \"Lyon\" for France instead of \"Paris\"!\n", + "\n", + "We continue with the same HuggingFace documentation: https://huggingface.co/docs/transformers/main_classes/model (section \"generate()\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60f16e18", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the fine-tuned model that we just trained\n", + "fine_tuned_model = ...\n", + "fine_tuned_tokenizer = ...\n", + "\n", + "print(\"✅ Fine-tuned model loaded!\\n\")\n", + "\n", + "# Comparison test: compare with the original model\n", + "print(\"Comparison with the original model (non fine-tuned GPT2):\")\n", + "print(\"=\" * 60)\n", + "\n", + "# Load the original model for comparison\n", + "original_model = GPT2LMHeadModel.from_pretrained(model_name)\n", + "original_tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n", + "original_tokenizer.pad_token = original_tokenizer.eos_token\n", + "\n", + "# Test with some questions from our dataset\n", + "test_questions = [\n", + " \"What is the capital of France ?\",\n", + "]\n", + "\n", + "for question in test_questions:\n", + " print(f\"\\n❓ Question: {question}\\n\")\n", + " \n", + " # Response from the ORIGINAL model\n", + " inputs_orig = original_tokenizer.encode(question, return_tensors='pt')\n", + " outputs_orig = original_model.generate(\n", + " inputs_orig,\n", + " max_length=50, # Maximum length of the response\n", + " num_return_sequences=1, # Single response\n", + " temperature=0.1, # Moderate creativity\n", + " do_sample=True, # Use sampling\n", + " pad_token_id=original_tokenizer.eos_token_id\n", + " )\n", + " response_orig = original_tokenizer.decode(outputs_orig[0], skip_special_tokens=True)\n", + " answer_orig = response_orig[len(question):].strip()\n", + " print(f\"💬 Response from ORIGINAL model : {answer_orig}\")\n", + " \n", + " # Response from the FINE-TUNED model\n", + " inputs_fine = fine_tuned_tokenizer.encode(question, return_tensors='pt')\n", + " outputs_fine = fine_tuned_model.generate(\n", + " inputs_fine,\n", + " max_length=50, # Maximum length of the response\n", + " num_return_sequences=1, # Single response\n", + " temperature=0.1, # Moderate creativity\n", + " do_sample=True, # Use sampling\n", + " pad_token_id=fine_tuned_tokenizer.eos_token_id\n", + " )\n", + " response_fine = fine_tuned_tokenizer.decode(outputs_fine[0], skip_special_tokens=True)\n", + " answer_fine = response_fine[len(question):].strip()\n", + " print(f\"💬 Response from FINE-TUNED model : {answer_fine}\")\n", + " \n", + " print(\"-\" * 60)\n", + "\n", + "print(\"\\n\" + \"=\" * 60)\n", + "print(\"\\n🎉 Congratulations! You have completed fine-tuning an LLM model!\")\n", + "print(\"\\nWhat you have accomplished:\")\n", + "print(\" ✅ You loaded a pre-trained model\")\n", + "print(\" ✅ You prepared your own data\")\n", + "print(\" ✅ You tokenized the data\")\n", + "print(\" ✅ You configured the training\")\n", + "print(\" ✅ You fine-tuned the model\")\n", + "print(\" ✅ You tested the model and saw the difference!\")\n", + "print(\"\\n🚀 Now you know how to adapt an AI model to your specific domain!\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "bd6e1c65", + "metadata": {}, + "source": [ + "# Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "**Congratulations!** You have completed this part on fine-tuning LLMs! \n", + "\n", + "You now know how to:\n", + "- Load an existing model (with Ollama or HuggingFace)\n", + "- Create and prepare your own data\n", + "- Tokenize data for the model\n", + "- Configure training\n", + "- Fine-tune an LLM model\n", + "- Test and compare results\n", + "\n", + "**Possible next steps (only do it if you finish the day):**\n", + "- Add more data to your dataset to improve results\n", + "- Experiment with different training parameters\n", + "- Try with other models (larger, smaller)\n", + "- Deploy your fine-tuned model somewhere\n", + "\n", + "**But now, you have a model that can give you false information with confidence.** This is interesting, but it also raises a question: 🚨 how can we check if a model's answer is actually correct or not?\n", + "\n", + "This is exactly the kind of challenge we'll look at in the next part. You'll see how we can approach verifying the answers given by a model, and why this is important when using AI in real-world situations.\n", + "\n", + "Let's continue exploring together in the next section." + ] + }, + { + "cell_type": "markdown", + "id": "3fc96efa", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/AI/Day02/2 - RAG/README.md b/AI/Day02/2 - RAG/README.md new file mode 100644 index 0000000..2ebe96e --- /dev/null +++ b/AI/Day02/2 - RAG/README.md @@ -0,0 +1,108 @@ +# RAG (Retrieval Augmented Generation) 🔍 + +Discover how to build a complete RAG system that gives an LLM access to your own documents to answer questions with accurate, sourced information. + +You will: +- Understand and create text embeddings with Sentence-Transformers +- Measure semantic similarity between texts using cosine similarity +- Visualize embeddings in 2D with PCA +- Build a vector database with ChromaDB +- Build a full RAG pipeline (Retrieve, Augment, Generate) with Ollama +- Implement document chunking for real-world documents + +## What is RAG? + +RAG (Retrieval Augmented Generation) is a technique that enhances an LLM by giving it access to external documents at query time. Instead of modifying the model's weights (like fine-tuning), we **search for relevant information** in a knowledge base and provide it as context to the model. + +Think of it this way: +- **Fine-tuning** = Teaching a student new facts by heart (slow, expensive, hard to update) +- **RAG** = Giving the student access to a library and teaching them how to search (fast, flexible, always up-to-date) + +In this workshop, you'll build a RAG system over documents about a fictional company (TechCorp) using embeddings, ChromaDB, and a local LLM via Ollama. + +## Documentation + +- [Sentence-Transformers Documentation](https://www.sbert.net/) +- [ChromaDB Documentation](https://docs.trychroma.com/) +- [Ollama API Documentation](https://github.com/ollama/ollama/blob/main/docs/api.md) +- [Scikit-learn PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) + +## Getting Started + +### Prerequisites + +- Python 3.7+ +- Jupyter Notebook installed +- Basic understanding of Python and machine learning concepts +- [Ollama](https://ollama.com/) installed on your machine + +### Installation + +Install the required packages: + +```bash +pip install sentence-transformers chromadb numpy matplotlib scikit-learn requests +``` + +Or use the installation cells in the notebook. + +### Ollama Setup + +1. Install Ollama from [ollama.com](https://ollama.com/) +2. Pull the model: +```bash +ollama pull llama3.2:3b +``` +3. Keep Ollama running in the background: +```bash +ollama serve +``` + +Please open the `rag_afternoon.ipynb` file to get started. + +## Workshop Structure + +1. **Understanding Embeddings**: Create text embeddings and measure semantic similarity +2. **Visualize Embeddings**: Use PCA to see how embeddings cluster by meaning +3. **Building a Vector Database**: Store and search documents with ChromaDB +4. **Building a RAG System**: Combine retrieval + LLM generation into a full pipeline +5. **RAG on Real Documents**: Implement chunking and build RAG over multi-file documents + +## Next Steps + +After completing this workshop, you can: +- Build a RAG system with your own documents (PDFs, web pages, etc.) +- Try different embedding models and compare retrieval quality +- Experiment with different chunk sizes and overlaps +- Combine RAG with fine-tuning for production-ready AI systems + +## Author + +This workshop introduces RAG techniques for giving LLMs access to external knowledge without retraining. + +

+Organization +

+
+

+ + LinkedIn logo + + + Instagram logo + + + Twitter logo + + + Discord logo + +

+

+ + Website logo + +

+ +> 🚀 Don't hesitate to follow us on our different networks, and put a star 🌟 on `PoC's` repositories. diff --git a/AI/Day02/2 - RAG/documents/company_overview.txt b/AI/Day02/2 - RAG/documents/company_overview.txt new file mode 100644 index 0000000..4a06385 --- /dev/null +++ b/AI/Day02/2 - RAG/documents/company_overview.txt @@ -0,0 +1,9 @@ +TechCorp: Company Overview + +TechCorp is a leading artificial intelligence company headquartered in San Francisco, California. Founded in 2020 by Alice Johnson and Bob Smith, the company has quickly established itself as a pioneer in the application of AI to healthcare. The two co-founders met while working at Google's DeepMind division, where they collaborated on medical imaging research. Frustrated by the slow adoption of AI in clinical settings, they decided to launch their own venture with a clear mission: make medical diagnosis more accurate, faster, and accessible to healthcare providers worldwide. + +Since its founding, TechCorp has experienced remarkable growth. The company started with just two people working out of a small office in the Mission District of San Francisco. By the end of 2021, the team had grown to 30 employees. In 2022, the headcount reached 80, and by late 2023, TechCorp employed over 150 people across two offices. The San Francisco headquarters houses the engineering and research teams, while the London office, opened in mid-2022, focuses on European business development and regulatory compliance. + +TechCorp's corporate culture emphasizes innovation, collaboration, and a patient-first mindset. Every quarter, the company hosts "Health Hack Week," where employees from all departments work on experimental projects that could improve patient outcomes. Several of TechCorp's current product features originated from these hackathon events. The company also maintains partnerships with three major university hospitals for clinical validation of its AI models. + +The leadership team includes CEO Alice Johnson, CTO Bob Smith, CFO Maria Garcia (formerly at Goldman Sachs), and VP of Product David Chen (formerly at Apple Health). The board of directors includes representatives from Sequoia Capital and two independent healthcare industry experts. diff --git a/AI/Day02/2 - RAG/documents/expansion_plans.txt b/AI/Day02/2 - RAG/documents/expansion_plans.txt new file mode 100644 index 0000000..c20ee31 --- /dev/null +++ b/AI/Day02/2 - RAG/documents/expansion_plans.txt @@ -0,0 +1,17 @@ +TechCorp: International Expansion and Future Plans + +Asia-Pacific Expansion (2024) +TechCorp announced its Asia-Pacific expansion strategy in October 2023. The company plans to open its first Asian office in Tokyo, Japan in Q1 2024, followed by a second office in Singapore in Q3 2024. Japan was chosen as the first market due to its aging population, advanced healthcare infrastructure, and strong demand for AI-assisted diagnostics. The Japanese healthcare market spends over $500 billion annually, and radiology departments face a significant shortage of qualified radiologists. + +The Singapore office will serve as a regional hub for Southeast Asia, covering markets including Malaysia, Thailand, Indonesia, and the Philippines. TechCorp has already signed a memorandum of understanding with Singapore's National University Health System (NUHS) for a pilot deployment of MedAI across three hospitals. The company expects to have 25 employees in Asia by end of 2024. + +Regulatory Strategy +Operating in multiple countries requires navigating complex regulatory landscapes. MedAI received FDA 510(k) clearance in the United States in March 2023, which was a major milestone. In Europe, the company obtained CE marking under the new EU Medical Device Regulation (MDR) in August 2023. For Japan, TechCorp is working with the Pharmaceuticals and Medical Devices Agency (PMDA) to obtain approval under the accelerated AI medical device pathway. The company expects Japanese regulatory approval by mid-2024. + +Research and Development Roadmap +TechCorp allocates approximately 40% of its revenue to research and development. The R&D team, led by CTO Bob Smith, consists of 60 engineers and researchers, including 15 with PhDs in machine learning, computer vision, or biomedical engineering. Current R&D priorities include improving MedAI's accuracy to 98% through larger training datasets, developing real-time analysis capabilities for surgical imaging, expanding PathAI to cover additional cancer types beyond breast cancer, and building a natural language interface that allows doctors to query patient imaging history using conversational commands. + +The company also maintains an active research publication program, with team members having published 12 peer-reviewed papers in top medical AI conferences and journals in 2023, including NeurIPS, MICCAI, and Nature Medicine. + +Awards and Recognition +TechCorp won the Best AI Startup award at TechCrunch Disrupt 2023. The company was also named to Forbes' AI 50 list for 2023 and received the Healthcare Innovation Award from the American Hospital Association. CEO Alice Johnson was featured in Time Magazine's 100 Most Influential People in AI for 2023. diff --git a/AI/Day02/2 - RAG/documents/financials.txt b/AI/Day02/2 - RAG/documents/financials.txt new file mode 100644 index 0000000..3fb51ae --- /dev/null +++ b/AI/Day02/2 - RAG/documents/financials.txt @@ -0,0 +1,17 @@ +TechCorp: Financial Information and Funding History + +Seed Round (2020) +TechCorp raised its initial seed funding of $2 million in August 2020, just three months after incorporation. The round was led by Y Combinator, with participation from several angel investors including former Google Health executives. This funding was used to build the initial prototype of MedAI and hire the first five engineers. + +Series A (2021) +In December 2021, TechCorp closed a $12 million Series A round led by Andreessen Horowitz (a16z). The round valued the company at $60 million. At this point, MedAI had completed its first clinical trial with promising results, and the company had signed letters of intent with four hospital systems. The Series A funding was allocated primarily to expanding the engineering team, scaling cloud infrastructure, and initiating the FDA approval process for MedAI. + +Series B (2023) +The most significant funding milestone came in June 2023, when TechCorp raised $50 million in a Series B round led by Sequoia Capital, with participation from existing investors a16z and Y Combinator. The company was valued at $300 million, a 5x increase from the Series A valuation. This round attracted significant attention in the healthcare AI space and was covered extensively by TechCrunch, Bloomberg, and CNBC. + +Revenue and Growth +TechCorp generated its first revenue in mid-2022 when MedAI became commercially available. The company's revenue for fiscal year 2022 was $10 million, primarily from subscription contracts with hospital systems in the United States. In 2023, revenue grew to $25 million, representing a 150% year-over-year increase. This growth was driven by expansion into European markets through the London office, the launch of MedAI Pro, and increasing adoption among mid-sized hospitals. The company projects revenue of $60 million for 2024, driven by the Asia expansion and the full launch of PathAI. + +TechCorp operates on a SaaS (Software as a Service) model, charging hospitals an annual subscription fee based on the number of imaging studies processed. Pricing ranges from $50,000 per year for small clinics to over $500,000 per year for large hospital networks using MedAI Pro. The company's gross margin is approximately 75%, which is strong for a healthcare SaaS business. + +The company has not yet reached profitability, with a net loss of $15 million in 2023. However, management expects to reach break-even by the end of 2025, assuming current growth rates continue and operating expenses are managed carefully. The $50 million Series B funding provides sufficient runway through 2026. diff --git a/AI/Day02/2 - RAG/documents/partnerships.txt b/AI/Day02/2 - RAG/documents/partnerships.txt new file mode 100644 index 0000000..8c2c5f1 --- /dev/null +++ b/AI/Day02/2 - RAG/documents/partnerships.txt @@ -0,0 +1,13 @@ +TechCorp: Partnerships and Collaborations + +Hospital Partnerships +TechCorp has established partnerships with over 40 hospitals across the United States and Europe. These partnerships range from pilot programs to full enterprise deployments. Key hospital partners include Stanford Medical Center, which was the first hospital to deploy MedAI in a clinical setting in January 2022. The collaboration with Stanford includes a joint research agreement for developing new AI models for cardiac imaging. Johns Hopkins Hospital joined as a partner in April 2022, contributing anonymized training data and providing clinical validation for new MedAI features. The Mayo Clinic signed an enterprise agreement for MedAI Pro in October 2023, deploying the system across its entire network of 23 hospitals. In Europe, TechCorp partners with University College London Hospitals (UCLH) and Charite Hospital in Berlin. + +Technology Partners +On the technology side, TechCorp maintains strategic partnerships with major cloud providers and healthcare IT companies. The company uses Google Cloud Platform as its primary infrastructure provider, leveraging Google's TPU chips for model training and inference. A partnership with Epic Systems, the largest electronic health records (EHR) provider in the US, allows MedAI to integrate seamlessly with hospital information systems. TechCorp also collaborates with NVIDIA on optimizing its deep learning models for edge deployment, enabling MedAI to run on local hospital hardware without requiring cloud connectivity for sensitive patient data. + +Academic Collaborations +TechCorp maintains active research collaborations with five universities. In addition to Stanford and Johns Hopkins, the company works with MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) on developing more interpretable AI models that can explain their diagnostic reasoning to physicians. A collaboration with the University of Oxford focuses on applying federated learning techniques to train AI models across multiple hospitals without sharing patient data. The University of Tokyo partnership, established in 2023, focuses on adapting MedAI for the Japanese healthcare context, including support for Japanese medical terminology and imaging standards. + +Industry Memberships +TechCorp is an active member of the Coalition for Health AI (CHAI), an industry group working to establish standards for responsible AI in healthcare. The company also participates in the Digital Therapeutics Alliance and the Healthcare Information and Management Systems Society (HIMSS). CEO Alice Johnson serves on the board of CHAI and regularly speaks at healthcare AI conferences about the importance of clinical validation and regulatory compliance for AI medical devices. diff --git a/AI/Day02/2 - RAG/documents/products.txt b/AI/Day02/2 - RAG/documents/products.txt new file mode 100644 index 0000000..73b2b16 --- /dev/null +++ b/AI/Day02/2 - RAG/documents/products.txt @@ -0,0 +1,17 @@ +TechCorp: Products and Technology + +MedAI - Diagnostic Assistant + +MedAI is TechCorp's flagship product, launched in early 2022. It is an AI-powered diagnostic assistant designed to help radiologists and physicians analyze medical images more efficiently and accurately. The system supports three types of medical imaging: X-rays, MRIs, and CT scans. In clinical trials conducted across 12 hospitals in the United States and Europe, MedAI achieved a diagnostic accuracy rate of 95%, which is comparable to the performance of experienced radiologists with over 15 years of practice. + +The technology behind MedAI is based on a proprietary deep learning architecture that combines convolutional neural networks with transformer-based attention mechanisms. The model was trained on a dataset of over 2 million anonymized medical images, carefully curated in partnership with Stanford Medical Center and Johns Hopkins Hospital. Each image in the training set was annotated by at least three board-certified radiologists to ensure label quality. + +MedAI integrates directly into hospital PACS (Picture Archiving and Communication System) workflows through standard DICOM protocols. This means doctors don't need to change their existing workflow to use the tool. The system highlights areas of concern in medical images, provides a confidence score for each finding, and generates a preliminary report that the radiologist can review and edit. Processing time is under 30 seconds per image, significantly faster than manual analysis which can take 15-20 minutes per case. + +MedAI Pro - Enterprise Solution + +In September 2023, TechCorp launched MedAI Pro, an enterprise version of the product designed for large hospital networks. MedAI Pro includes all features of the standard version plus advanced analytics dashboards, multi-site deployment support, custom model fine-tuning for specific patient populations, and a collaboration module that allows radiologists across different locations to discuss cases in real-time. The enterprise solution also includes a dedicated support team and guaranteed 99.9% uptime SLA. + +PathAI - Pathology Module (Beta) + +TechCorp announced PathAI in November 2023, a new module focused on digital pathology. Still in beta testing, PathAI analyzes tissue sample images to assist pathologists in detecting cancerous cells. Early results from pilot programs at three hospitals show a 92% accuracy rate for detecting breast cancer in biopsy samples. The full launch is planned for Q2 2024. diff --git a/AI/Day02/2 - RAG/rag_afternoon.ipynb b/AI/Day02/2 - RAG/rag_afternoon.ipynb new file mode 100644 index 0000000..281a9dc --- /dev/null +++ b/AI/Day02/2 - RAG/rag_afternoon.ipynb @@ -0,0 +1,966 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro-welcome", + "metadata": {}, + "source": [ + "Hello **Everyone** ! \n", + "Welcome to the **second part of Day 2** of our AI pool ! \n", + "\n", + "This morning, you learned how to **fine-tune** a model : you modified GPT-2's weights so it would \"memorize\" new facts (like false capitals). That works, but it has limits.\n", + "\n", + "This afternoon, we take a completely different approach. Instead of modifying the model, we will **give it access to external documents** and teach it to find the right information before answering.\n", + "\n", + "Our goal : build a system that can answer questions about **your own documents** (PDFs, texts, etc.) with accurate, sourced information. \n", + "\n", + "By the end of this session, you will have built a complete **RAG** system (Retrieval Augmented Generation) - one of the most widely used techniques in production AI applications today." + ] + }, + { + "cell_type": "markdown", + "id": "context-problem", + "metadata": {}, + "source": [ + "**But wait... why not just fine-tune again ?**\n", + "\n", + "Remember this morning ? We fine-tuned GPT-2 to give us false capitals. The model \"learned\" these false facts by modifying its internal weights. \n", + "But there are real problems with this approach :\n", + "- **What if the information changes ?** You would need to re-train every time.\n", + "- **What if you have thousands of documents** that update every day ? Fine-tuning is too slow and expensive for that.\n", + "- **Fine-tuning is permanent** : once the model learns something wrong, it's hard to \"un-learn\" it.\n", + "\n", + "We need a smarter approach : **RAG** (Retrieval Augmented Generation). \n", + "\n", + "Think of it like this :\n", + "- **Fine-tuning** = Teaching a student new facts by heart (slow, expensive, hard to update)\n", + "- **RAG** = Giving the student access to a library and teaching them how to search (fast, flexible, always up-to-date)\n", + "\n", + "With RAG, the model itself doesn't change. Instead, we **search for relevant information** in our documents and give it to the model along with the question. The model then uses that information to generate an accurate answer.\n", + "\n", + "But before building RAG, we need to understand **embeddings** - the technology that makes intelligent search possible !" + ] + }, + { + "cell_type": "markdown", + "id": "part1-title", + "metadata": {}, + "source": [ + "# **I/ Understanding Embeddings**" + ] + }, + { + "cell_type": "markdown", + "id": "embedding-intro", + "metadata": {}, + "source": [ + "### **What is an Embedding ?**\n", + "\n", + "An embedding is a way to represent text (or images, audio...) as a **list of numbers** (a vector). \n", + "\n", + "Imagine you want to organize books in a library. Instead of organizing them alphabetically, you organize them by **meaning** :\n", + "- Books about cooking are close together\n", + "- Books about space are close together\n", + "- A book about \"cooking in space\" would be somewhere in between\n", + "\n", + "Embeddings do exactly this : texts with similar meanings have similar numbers (vectors that are \"close\" in space).\n", + "\n", + "**Example :**\n", + "- \"I love pizza\" → [0.2, 0.8, 0.1, ...]\n", + "- \"Pizza is my favorite food\" → [0.21, 0.79, 0.12, ...] (very similar)\n", + "- \"The weather is nice\" → [0.9, 0.1, 0.7, ...] (very different)" + ] + }, + { + "cell_type": "markdown", + "id": "install-packages", + "metadata": {}, + "source": [ + "### ***1/ Setup: Install the necessary packages***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "install-cell", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install sentence-transformers chromadb numpy" + ] + }, + { + "cell_type": "markdown", + "id": "create-embedding-title", + "metadata": {}, + "source": [ + "### ***2/ Create your first embedding***\n", + "\n", + "We will use a pre-trained model from HuggingFace to create embeddings. \n", + "The model `all-MiniLM-L6-v2` is small, fast, and works great for most use cases.\n", + "\n", + "**Wait, another library ?** This morning we used `transformers` from HuggingFace to load GPT-2 (a text generation model). Now we use `sentence-transformers`, which is built on top of `transformers` but specialized for creating **embeddings**. Think of it this way :\n", + "- `transformers` = general-purpose (text generation, classification, translation...)\n", + "- `sentence-transformers` = specialized for turning text into vectors (embeddings)\n", + "\n", + "They both come from HuggingFace, but have different purposes.\n", + "\n", + "**Documentation :** https://www.sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "first-embedding", + "metadata": {}, + "outputs": [], + "source": [ + "from sentence_transformers import SentenceTransformer\n", + "\n", + "# TODO: Load the embedding model 'all-MiniLM-L6-v2'\n", + "embedding_model = ...\n", + "\n", + "text = \"I love artificial intelligence\"\n", + "\n", + "# TODO: Create the embedding\n", + "embedding = ...\n", + "\n", + "print(f\"Embedding created.\")\n", + "print(f\"Embedding dimension: {len(embedding)}\")\n", + "print(f\"First 10 values: {embedding[:10]}\")" + ] + }, + { + "cell_type": "markdown", + "id": "similarity-title", + "metadata": {}, + "source": [ + "### ***3/ Measure similarity between texts***\n", + "\n", + "Now comes the magic. We can measure how **similar** two texts are by comparing their embeddings. \n", + "We use **cosine similarity** : a score between -1 and 1.\n", + "\n", + "**How to read the score :**\n", + "- **1.0** = Identical meaning (the two texts say the same thing)\n", + "- **0.7 - 0.9** = Very similar (related topics, similar ideas)\n", + "- **0.3 - 0.7** = Somewhat related\n", + "- **0.0** = No relation at all\n", + "- **-1.0** = Opposite meaning\n", + "\n", + "**Intuition :** Imagine each embedding as an arrow in space. Cosine similarity measures the **angle** between two arrows. If they point in the same direction (angle = 0), the similarity is 1. If they point in opposite directions (angle = 180), it's -1.\n", + "\n", + "**Documentation :** https://www.sbert.net/docs/package_reference/util.html\n", + "\n", + "**Your task :** Complete the code to calculate similarity between sentences." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "similarity-calc", + "metadata": {}, + "outputs": [], + "source": [ + "from sentence_transformers import util\n", + "\n", + "sentences = [\n", + " \"I love programming in Python\",\n", + " \"Python is my favorite programming language\",\n", + " \"The weather is beautiful today\",\n", + " \"I enjoy coding and building software\"\n", + "]\n", + "\n", + "# TODO: Create embeddings for all sentences\n", + "embeddings = ...\n", + "\n", + "print(\"Similarity with 'I love programming in Python':\\n\")\n", + "\n", + "for i, sentence in enumerate(sentences):\n", + " # TODO: Calculate cosine similarity between first embedding and current one\n", + " similarity = ...\n", + " print(f\" \\\"{sentence}\\\"\")\n", + " print(f\" → Similarity: {similarity:.4f}\\n\")" + ] + }, + { + "cell_type": "markdown", + "id": "question-similarity", + "metadata": {}, + "source": [ + "**Question :** Which sentences have the highest similarity ? Does it make sense to you ? \n", + "Take a moment to analyze the results before continuing." + ] + }, + { + "cell_type": "markdown", + "id": "visualize-title", + "metadata": {}, + "source": [ + "### ***4/ Visualize embeddings in 2D***\n", + "\n", + "We said that embeddings place similar texts \"close together\" in space. Let's actually **see** this !\n", + "\n", + "The embeddings we created have 384 dimensions - impossible to visualize directly. But we can use a technique called **PCA** (Principal Component Analysis) to compress them down to just 2 dimensions, so we can plot them on a graph.\n", + "\n", + "This is a great way to build intuition about how embeddings organize text by meaning.\n", + "\n", + "**Documentation :** https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "visualize-embeddings", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install matplotlib scikit-learn\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.decomposition import PCA\n", + "import numpy as np\n", + "\n", + "texts = [\n", + " # Tech topic\n", + " \"I love programming in Python\",\n", + " \"JavaScript is great for web development\",\n", + " \"Machine learning is fascinating\",\n", + " # Food topic \n", + " \"Pizza is my favorite food\",\n", + " \"I love cooking Italian pasta\",\n", + " \"Sushi is delicious\",\n", + " # Nature topic\n", + " \"The mountains are beautiful\",\n", + " \"I love hiking in the forest\",\n", + " \"The ocean is peaceful\"\n", + "]\n", + "\n", + "# TODO: Create embeddings for all texts\n", + "text_embeddings = ...\n", + "\n", + "# TODO: Reduce to 2D using PCA\n", + "pca = ...\n", + "embeddings_2d = ...\n", + "\n", + "plt.figure(figsize=(12, 8))\n", + "colors = ['blue', 'blue', 'blue', 'red', 'red', 'red', 'green', 'green', 'green']\n", + "\n", + "for i, (x, y) in enumerate(embeddings_2d):\n", + " plt.scatter(x, y, c=colors[i], s=100)\n", + " plt.annotate(texts[i][:30] + \"...\", (x, y), fontsize=8)\n", + "\n", + "plt.title(\"Embeddings visualized in 2D (Blue=Tech, Red=Food, Green=Nature)\")\n", + "plt.xlabel(\"Dimension 1\")\n", + "plt.ylabel(\"Dimension 2\")\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()\n", + "\n", + "print(\"\\nNotice how similar topics cluster together.\")" + ] + }, + { + "cell_type": "markdown", + "id": "part2-title", + "metadata": {}, + "source": [ + "# **II/ Building a Vector Database**\n", + "\n", + "Now that you understand how embeddings capture meaning as numbers, the next question is : **how do we search through thousands of embeddings quickly ?**\n", + "\n", + "When you have 10 documents, you can compare them one by one. But with 10,000 or 1,000,000 documents, you need something smarter. That's where **vector databases** come in." + ] + }, + { + "cell_type": "markdown", + "id": "vectordb-intro", + "metadata": {}, + "source": [ + "Now that we understand embeddings, we need a place to **store** and **search** them efficiently.\n", + "\n", + "**Why not just use a Python list ?** \n", + "You could store embeddings in a list and loop through all of them to find the most similar one. But this gets **very slow** when you have thousands or millions of documents. Imagine comparing your query against 1 million vectors one by one - that would take forever.\n", + "\n", + "A **vector database** is a specialized database designed to :\n", + "- Store embeddings (lists of numbers) efficiently\n", + "- Find the most similar vectors **extremely fast**, even with millions of entries (using smart indexing algorithms)\n", + "- Handle metadata (like the source file, date, author...) alongside each vector\n", + "\n", + "We will use **ChromaDB**, which is simple, works locally, and is perfect for learning.\n", + "\n", + "**Important concept :** In Part I, we manually created embeddings using `SentenceTransformer`. ChromaDB can do this **automatically** for you ! When you add a text document, ChromaDB will create its embedding behind the scenes using its own built-in embedding model (which happens to be `all-MiniLM-L6-v2` - the same one we used in Part I !).\n", + "\n", + "So Part I taught you **how embeddings work under the hood**. Now ChromaDB will handle the embedding step for us, so we can focus on building the search and retrieval pipeline.\n", + "\n", + "**Other popular vector databases :** Pinecone, Weaviate, Qdrant, FAISS, Milvus" + ] + }, + { + "cell_type": "markdown", + "id": "create-db-title", + "metadata": {}, + "source": [ + "### ***1/ Create a ChromaDB collection***\n", + "\n", + "A \"collection\" in ChromaDB is like a table in a regular database. \n", + "It stores your documents and their embeddings together.\n", + "\n", + "When you create a collection, ChromaDB is ready to :\n", + "1. Accept documents (plain text)\n", + "2. Automatically convert them to embeddings\n", + "3. Store both the text and its embedding\n", + "4. Later, search for similar documents when you ask a question\n", + "\n", + "**Documentation :** https://docs.trychroma.com/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "create-chromadb", + "metadata": {}, + "outputs": [], + "source": [ + "import chromadb\n", + "\n", + "# TODO: Create a ChromaDB client\n", + "chroma_client = ...\n", + "\n", + "# TODO: Create a collection named \"my_knowledge_base\"\n", + "collection = ...\n", + "\n", + "print(f\"Collection '{collection.name}' created.\")\n", + "print(f\"Currently contains {collection.count()} documents\")" + ] + }, + { + "cell_type": "markdown", + "id": "add-docs-title", + "metadata": {}, + "source": [ + "### ***2/ Add documents to the database***\n", + "\n", + "Let's add some documents about a fictional company. \n", + "Later, we will ask questions and retrieve relevant information.\n", + "\n", + "**Your task :** Add documents to the collection using the `add()` method.\n", + "\n", + "**Hint :** ChromaDB requires each document to have a **unique string ID**. \n", + "Check the documentation to see how the `add()` method works : https://docs.trychroma.com/docs/collections/add-data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "add-documents", + "metadata": {}, + "outputs": [], + "source": [ + "documents = [\n", + " \"TechCorp was founded in 2020 by Alice Johnson and Bob Smith in San Francisco.\",\n", + " \"TechCorp specializes in artificial intelligence solutions for healthcare.\",\n", + " \"The company has 150 employees and offices in San Francisco and London.\",\n", + " \"TechCorp's main product is MedAI, a diagnostic assistant for doctors.\",\n", + " \"In 2023, TechCorp raised $50 million in Series B funding from Sequoia Capital.\",\n", + " \"The CEO of TechCorp is Alice Johnson, who previously worked at Google.\",\n", + " \"TechCorp's revenue in 2023 was $25 million, a 150% increase from 2022.\",\n", + " \"The company plans to expand to Asia in 2024, starting with Japan and Singapore.\",\n", + " \"MedAI can analyze X-rays, MRIs, and CT scans with 95% accuracy.\",\n", + " \"TechCorp won the Best AI Startup award at TechCrunch Disrupt 2023.\"\n", + "]\n", + "\n", + "# TODO: Add documents to the collection with unique IDs\n", + "...\n", + "\n", + "print(f\"Added {collection.count()} documents to the collection.\")" + ] + }, + { + "cell_type": "markdown", + "id": "query-title", + "metadata": {}, + "source": [ + "### ***3/ Search for relevant documents***\n", + "\n", + "Now the magic happens. We can search for documents by **meaning**, not just keywords. \n", + "The database will find documents that are semantically similar to our query.\n", + "\n", + "**Your task :** Use the `query()` method to search for relevant documents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "query-documents", + "metadata": {}, + "outputs": [], + "source": [ + "query = \"Who founded the company and when ?\"\n", + "\n", + "# TODO: Query the collection and get 3 results\n", + "results = ...\n", + "\n", + "print(f\"Query: \\\"{query}\\\"\\n\")\n", + "print(\"Most relevant documents:\")\n", + "for i, doc in enumerate(results['documents'][0]):\n", + " print(f\" {i+1}. {doc}\")" + ] + }, + { + "cell_type": "markdown", + "id": "experiment-title", + "metadata": {}, + "source": [ + "### ***4/ Experiment with different queries***\n", + "\n", + "Try different questions and see how the system finds relevant documents. \n", + "Notice how it understands meaning, not just exact word matches." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "experiment-queries", + "metadata": {}, + "outputs": [], + "source": [ + "test_queries = [\n", + " \"What does the company sell ?\",\n", + " \"How much money did they raise ?\",\n", + " \"Where are the offices located ?\",\n", + " \"Tell me about the medical AI product\"\n", + "]\n", + "\n", + "for query in test_queries:\n", + " # TODO: Query the collection with 2 results\n", + " results = ...\n", + " \n", + " print(f\"\\nQuery: \\\"{query}\\\"\")\n", + " print(\"Results:\")\n", + " for doc in results['documents'][0]:\n", + " print(f\" → {doc}\")\n", + " print(\"-\" * 60)" + ] + }, + { + "cell_type": "markdown", + "id": "part3-title", + "metadata": {}, + "source": [ + "# **III/ Building a RAG System**" + ] + }, + { + "cell_type": "markdown", + "id": "rag-intro", + "metadata": {}, + "source": [ + "In Part II, we built a system that can **find relevant documents** based on a question. That's great, but it only gives us raw text snippets - it doesn't actually **answer** the question.\n", + "\n", + "Now we combine everything into a complete **RAG** (Retrieval Augmented Generation) system by adding an LLM to the pipeline.\n", + "\n", + "**How RAG works - the 3 steps :**\n", + "1. **Retrieve** : The user asks a question. We search our vector database for the most relevant documents.\n", + "2. **Augment** : We take those documents and insert them into a prompt, along with the question. This gives the LLM the context it needs.\n", + "3. **Generate** : The LLM reads the context and generates an answer based **only on the provided documents**.\n", + "\n", + "**Why is this powerful ?**\n", + "- The LLM has access to **your specific data** (company docs, internal knowledge...)\n", + "- Answers are **grounded** in real documents, which reduces hallucination\n", + "- You can **update** the knowledge base anytime without retraining the model\n", + "- You can trace **which documents** were used to generate each answer" + ] + }, + { + "cell_type": "markdown", + "id": "setup-llm-title", + "metadata": {}, + "source": [ + "### ***1/ Setup the LLM***\n", + "\n", + "For RAG, we need an LLM that can read our documents and generate an answer. We will use **Ollama** to run a local LLM.\n", + "\n", + "**Why Ollama and not HuggingFace like this morning ?** \n", + "This morning, we used HuggingFace `transformers` to load GPT-2 because we needed to **modify** the model (fine-tuning). Here, we don't need to modify anything - we just need to **send a prompt and get a response**. Ollama makes this very simple :\n", + "- It handles downloading and running LLMs with a single command\n", + "- It provides a simple HTTP API (like a web server) that we can call from Python\n", + "- It supports powerful models like Llama 3.2 that are much better than GPT-2 for generating answers\n", + "\n", + "Think of Ollama as a \"model server\" : it runs in the background and we send it questions via HTTP requests.\n", + "\n", + "**Setup steps :**\n", + "\n", + "1. Install Ollama from [ollama.com](https://ollama.com/)\n", + "2. Open a **separate terminal** and run :\n", + "```bash\n", + "ollama pull llama3.2:3b\n", + "```\n", + "3. Keep Ollama running in the background :\n", + "```bash\n", + "ollama serve\n", + "```\n", + "\n", + "**Troubleshooting :**\n", + "- If you get a \"connection refused\" error later, it means Ollama is not running. Start it with `ollama serve` in a terminal.\n", + "- If `ollama pull` fails, check your internet connection.\n", + "- The model is ~2GB, so the first download may take a few minutes.\n", + "\n", + "**Documentation :** https://github.com/ollama/ollama/blob/main/docs/api.md#generate-a-completion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "setup-llm", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install requests\n", + "\n", + "import requests\n", + "\n", + "LLM_URL = \"http://localhost:11434/api/generate\"\n", + "LLM_MODEL = \"llama3.2:3b\"\n", + "\n", + "# Test the connection to Ollama\n", + "try:\n", + " test_response = requests.get(\"http://localhost:11434/api/tags\", timeout=5)\n", + " if test_response.status_code == 200:\n", + " models = [m[\"name\"] for m in test_response.json().get(\"models\", [])]\n", + " print(f\"Ollama is running !\")\n", + " print(f\"Available models: {models}\")\n", + " if not any(LLM_MODEL in m for m in models):\n", + " print(f\"\\nWARNING: '{LLM_MODEL}' not found. Run: ollama pull {LLM_MODEL}\")\n", + " else:\n", + " print(f\"Model '{LLM_MODEL}' is ready.\")\n", + "except requests.exceptions.ConnectionError:\n", + " print(\"ERROR: Cannot connect to Ollama !\")\n", + " print(\"Make sure Ollama is running: open a terminal and run 'ollama serve'\")\n", + " print(\"Then run: ollama pull llama3.2:3b\")" + ] + }, + { + "cell_type": "markdown", + "id": "rag-function-title", + "metadata": {}, + "source": [ + "### ***2/ Build the RAG pipeline***\n", + "\n", + "Let's create a function that implements the 3 RAG steps :\n", + "1. **Retrieve** : Query ChromaDB to find relevant documents\n", + "2. **Augment** : Build a prompt that includes the retrieved documents as context\n", + "3. **Generate** : Send the prompt to Ollama and get the answer\n", + "\n", + "**Your task :** Complete the RAG function below.\n", + "\n", + "**Hint 1 - Building the context :** You need to combine the retrieved documents into a single block of text that the LLM can read.\n", + "\n", + "**Hint 2 - Structuring the prompt :** A good RAG prompt should :\n", + "- Clearly separate the context (retrieved documents) from the question\n", + "- Instruct the LLM to answer **only** based on the provided context\n", + "- Handle the case where the answer is not in the context\n", + "\n", + "Think about what instructions you would give to a human if you handed them a stack of documents and asked them a question.\n", + "\n", + "**Hint 3 - Calling Ollama :** Ollama exposes a REST API. You need to send a **POST** request with a JSON body containing the model name and your prompt. Make sure to set `\"stream\": False` to get the full response at once (otherwise Ollama streams the response token by token in a different format).\n", + "\n", + "**Documentation :** https://github.com/ollama/ollama/blob/main/docs/api.md#generate-a-completion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "rag-function", + "metadata": {}, + "outputs": [], + "source": [ + "def ask_with_rag(question: str, n_results: int = 3) -> tuple[str, list]:\n", + " \"\"\"\n", + " RAG pipeline: Retrieve relevant docs and generate an answer.\n", + " \n", + " Args:\n", + " question: The user's question\n", + " n_results: Number of documents to retrieve\n", + " \n", + " Returns:\n", + " Tuple of (answer, source documents)\n", + " \"\"\"\n", + " \n", + " # Step 1 - RETRIEVE: Query the collection to find relevant documents\n", + " results = ...\n", + " \n", + " # Step 2 - AUGMENT: Build the context string from retrieved documents\n", + " context = ...\n", + " \n", + " # Step 3 - AUGMENT: Create the prompt with context + question\n", + " prompt = ...\n", + " \n", + " # Step 4 - GENERATE: Call Ollama API and extract the response\n", + " response = ...\n", + " answer = ...\n", + " \n", + " return answer, results['documents'][0]\n", + "\n", + "print(\"RAG function created.\")" + ] + }, + { + "cell_type": "markdown", + "id": "test-rag-title", + "metadata": {}, + "source": [ + "### ***3/ Test your RAG system***\n", + "\n", + "Now let's test our RAG system with various questions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "test-rag", + "metadata": {}, + "outputs": [], + "source": [ + "test_questions = [\n", + " \"Who is the CEO of TechCorp ?\",\n", + " \"What is MedAI and what can it do ?\",\n", + " \"How much funding did the company raise ?\",\n", + "]\n", + "\n", + "for question in test_questions:\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"Question: {question}\\n\")\n", + " \n", + " try:\n", + " answer, sources = ask_with_rag(question)\n", + " print(f\"Answer: {answer}\")\n", + " print(f\"\\nSources used:\")\n", + " for source in sources:\n", + " print(f\" - {source}\")\n", + " except requests.exceptions.ConnectionError:\n", + " print(\"ERROR: Cannot connect to Ollama.\")\n", + " print(\"Open a terminal and run: ollama serve\")\n", + " except KeyError as e:\n", + " print(f\"ERROR: Unexpected response format from Ollama: {e}\")\n", + " print(\"Make sure the model is downloaded: ollama pull llama3.2:3b\")\n", + " except Exception as e:\n", + " print(f\"ERROR: {e}\")\n", + " print(\"Check that Ollama is running and the model is available.\")" + ] + }, + { + "cell_type": "markdown", + "id": "compare-title", + "metadata": {}, + "source": [ + "### ***4/ Compare: With RAG vs Without RAG***\n", + "\n", + "Let's see the difference between asking the LLM directly vs using RAG. \n", + "This shows why RAG is so powerful for domain-specific questions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "compare-rag", + "metadata": {}, + "outputs": [], + "source": [ + "def ask_without_rag(question: str) -> str:\n", + " \"\"\"Ask the LLM directly without any context.\"\"\"\n", + " # TODO: Send the question directly to Ollama, without any context documents\n", + " # Hint: same pattern as before, but the prompt is just the question itself\n", + " ...\n", + "\n", + "question = \"Who is the CEO of TechCorp and what is their background ?\"\n", + "\n", + "print(f\"Question: {question}\\n\")\n", + "print(\"=\" * 60)\n", + "\n", + "try:\n", + " print(\"\\nWITHOUT RAG (LLM has no context about TechCorp):\")\n", + " print(ask_without_rag(question))\n", + " \n", + " print(\"\\n\" + \"=\" * 60)\n", + " print(\"\\nWITH RAG (LLM receives relevant documents as context):\")\n", + " answer, _ = ask_with_rag(question)\n", + " print(answer)\n", + "except requests.exceptions.ConnectionError:\n", + " print(\"ERROR: Cannot connect to Ollama. Run 'ollama serve' in a terminal.\")\n", + "except Exception as e:\n", + " print(f\"ERROR: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "part4-title", + "metadata": {}, + "source": [ + "# **IV/ RAG on Real Documents : Chunking & Multi-File Pipeline**" + ] + }, + { + "cell_type": "markdown", + "id": "chunking-intro", + "metadata": {}, + "source": [ + "### ***1/ Understanding chunking***\n", + "\n", + "In Parts II and III, we worked with short, single-sentence documents. That made things easy. \n", + "But in real applications, your knowledge base is made of **long documents** : PDFs, reports, articles, internal docs...\n", + "\n", + "You can't just embed an entire 10-page document as a single vector. Why ?\n", + "- Embeddings work best on **short texts** (a few sentences). A single embedding for a whole document would lose the details.\n", + "- When you retrieve a long document, most of it is **irrelevant** to the question. You'd waste the LLM's context window.\n", + "- LLMs have **context limits** - you can't feed them an entire book.\n", + "\n", + "The solution is **chunking** : splitting long documents into smaller, meaningful pieces.\n", + "\n", + "**How chunking works :**\n", + "- We define a **maximum chunk size** (e.g. 500 characters).\n", + "- We walk through the text and cut at approximately every 500 characters.\n", + "- But we don't cut in the middle of a sentence ! We look for the **last sentence boundary** (period, exclamation mark...) before the limit, so each chunk contains **complete sentences**.\n", + "- We also add an **overlap** between chunks (e.g. 100 characters). This means the end of one chunk is repeated at the start of the next one. This prevents losing context at the boundaries - if an important fact spans two chunks, the overlap ensures it appears fully in at least one of them.\n", + "\n", + "**Example with chunk_size=500, overlap=100 :**\n", + "```\n", + "Document: \"Sentence A. Sentence B. Sentence C. Sentence D. Sentence E. ...\"\n", + "\n", + "Chunk 1: \"Sentence A. Sentence B. Sentence C.\" (480 chars, cut at last period before 500)\n", + "Chunk 2: \"Sentence C. Sentence D. Sentence E.\" (starts 100 chars before the end of chunk 1)\n", + "```\n", + "\n", + "In the `documents/` folder, you will find **5 text files** about TechCorp. \n", + "Your task is to implement the chunking function, load the files, chunk them, and build a complete RAG system over real documents.\n", + "\n", + "**Hint :** Python's `str` methods like `rfind()` can help you find sentence boundaries within a range of text." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "chunking-function", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "def chunk_text(text: str, chunk_size: int = 500, overlap: int = 100) -> list:\n", + " \"\"\"\n", + " Split text into overlapping chunks, cutting at sentence boundaries.\n", + " \n", + " Args:\n", + " text: The full text to chunk\n", + " chunk_size: Maximum size of each chunk (in characters)\n", + " overlap: Number of characters to overlap between chunks\n", + " \n", + " Returns:\n", + " List of text chunks\n", + " \"\"\"\n", + " chunks = []\n", + " start = 0\n", + " \n", + " while start < len(text):\n", + " # TODO: Find where this chunk should end.\n", + " # - Don't exceed chunk_size characters\n", + " # - Try to cut at a sentence boundary (not in the middle of a sentence)\n", + " end = ...\n", + " \n", + " # TODO: Extract the chunk, add it to the list, and advance start\n", + " # - Don't forget the overlap when moving start forward\n", + " # - Make sure you can't get stuck in an infinite loop\n", + " ...\n", + " \n", + " return chunks\n", + "\n", + "\n", + "# --- Load all .txt files from the documents/ folder ---\n", + "documents_dir = \"documents\"\n", + "all_chunks = []\n", + "chunk_sources = []\n", + "\n", + "for filename in sorted(os.listdir(documents_dir)):\n", + " if not filename.endswith(\".txt\"):\n", + " continue\n", + " \n", + " filepath = os.path.join(documents_dir, filename)\n", + " with open(filepath, \"r\") as f:\n", + " content = f.read()\n", + " \n", + " # TODO: Chunk the file content using the chunk_text function\n", + " file_chunks = ...\n", + " \n", + " for chunk in file_chunks:\n", + " all_chunks.append(chunk)\n", + " chunk_sources.append(filename)\n", + " \n", + " print(f\"Loaded '{filename}' -> {len(file_chunks)} chunks\")\n", + "\n", + "print(f\"\\nTotal: {len(all_chunks)} chunks from {len(set(chunk_sources))} files\")\n", + "print(f\"\\nExample chunk (chunk #1):\")\n", + "print(f\" Source: {chunk_sources[0]}\")\n", + "print(f\" Length: {len(all_chunks[0])} chars\")\n", + "print(f\" Content: \\\"{all_chunks[0][:150]}...\\\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "hu04m2nke1s", + "metadata": {}, + "source": [ + "### ***2/ Store chunks in a vector database***\n", + "\n", + "Now that we have chunks from multiple files, let's store them in a **new ChromaDB collection** and build a full RAG system over real documents.\n", + "\n", + "**Your task :** Add all chunks to a new collection, keeping track of which file each chunk came from (using **metadata**)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sje1b7j0hds", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create a new ChromaDB collection named \"techcorp_docs\"\n", + "docs_collection = ...\n", + "\n", + "# TODO: Add all chunks to the collection\n", + "# Each chunk needs: a unique ID, the chunk text as document, and metadata with the source filename\n", + "# Hint: metadata is a list of dicts, e.g. [{\"source\": \"file1.txt\"}, {\"source\": \"file2.txt\"}, ...]\n", + "...\n", + "\n", + "print(f\"Stored {docs_collection.count()} chunks in the 'techcorp_docs' collection.\")" + ] + }, + { + "cell_type": "markdown", + "id": "0nbnuf5f9kg", + "metadata": {}, + "source": [ + "### ***3/ RAG over real documents***\n", + "\n", + "Let's test our complete pipeline : **chunked documents + vector DB + LLM**. \n", + "The questions below require information spread across different files. Only a RAG system with proper chunking can answer them accurately." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4wy06z9srmu", + "metadata": {}, + "outputs": [], + "source": [ + "def ask_docs(question: str, n_results: int = 5) -> tuple[str, list]:\n", + " \"\"\"RAG pipeline over the chunked documents collection.\"\"\"\n", + " \n", + " # TODO: Query the docs_collection for relevant chunks\n", + " results = ...\n", + " \n", + " # TODO: Build context from retrieved chunks\n", + " context = ...\n", + " \n", + " # TODO: Create a prompt (same structure as ask_with_rag - context + question + instruction)\n", + " prompt = ...\n", + " \n", + " response = requests.post(LLM_URL, json={\n", + " \"model\": LLM_MODEL,\n", + " \"prompt\": prompt,\n", + " \"stream\": False\n", + " })\n", + " answer = response.json()[\"response\"]\n", + " \n", + " return answer, results['documents'][0], results['metadatas'][0]\n", + "\n", + "\n", + "test_questions = [\n", + " \"What is TechCorp's revenue growth from 2022 to 2023 ?\",\n", + " \"Which hospitals are partners of TechCorp ?\",\n", + " \"What is PathAI and when will it launch ?\",\n", + " \"How does MedAI integrate into hospital workflows ?\",\n", + " \"What is TechCorp's expansion plan for Asia ?\",\n", + "]\n", + "\n", + "for question in test_questions:\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"Question: {question}\\n\")\n", + " \n", + " try:\n", + " answer, sources, metadatas = ask_docs(question)\n", + " print(f\"Answer: {answer}\")\n", + " print(f\"\\nSources:\")\n", + " for source, meta in zip(sources, metadatas):\n", + " print(f\" [{meta['source']}] {source[:80]}...\")\n", + " except requests.exceptions.ConnectionError:\n", + " print(\"ERROR: Cannot connect to Ollama. Run 'ollama serve' in a terminal.\")\n", + " except Exception as e:\n", + " print(f\"ERROR: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "conclusion-title", + "metadata": {}, + "source": [ + "# **Conclusion**" + ] + }, + { + "cell_type": "markdown", + "id": "conclusion-content", + "metadata": {}, + "source": [ + "---\n", + "\n", + "**Congratulations !** You have completed this afternoon's session on RAG.\n", + "\n", + "**What you learned today :**\n", + "\n", + "- **Embeddings** : Transform text into vectors that capture meaning \n", + "- **Cosine similarity** : Measure how close two texts are in meaning \n", + "- **Vector Databases** : Store and search embeddings efficiently (ChromaDB) \n", + "- **RAG Pipeline** : Retrieve relevant documents + Generate answers with an LLM \n", + "- **Chunking** : Split large documents into smaller pieces for better retrieval \n", + "\n", + "**Key takeaways :**\n", + "- RAG lets you give LLMs access to **your specific data** without retraining\n", + "- Embeddings enable **semantic search** (by meaning, not just keywords)\n", + "- The quality of your RAG system depends on **how you chunk** your documents and **how you write your prompt**\n", + "\n", + "**What's next ? Ideas to explore :**\n", + "- Build a RAG system with your own documents (PDFs, web pages)\n", + "- Try different embedding models and compare results\n", + "- Experiment with different chunk sizes and overlaps to see how it affects quality\n", + "---\n", + "\n", + "**Combining this morning and this afternoon :** \n", + "This morning you learned **fine-tuning** (adapting model weights to learn new behavior). \n", + "This afternoon you learned **RAG** (giving the model external knowledge at query time). \n", + "\n", + "In practice, production AI systems often use **both** :\n", + "- **Fine-tune** for style, format, or domain-specific language (how the model speaks)\n", + "- **RAG** for factual, up-to-date information (what the model knows)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/AI/Day02/README.md b/AI/Day02/README.md new file mode 100644 index 0000000..56f6502 --- /dev/null +++ b/AI/Day02/README.md @@ -0,0 +1,28 @@ +# ~ PoC AI Pool 2025 ~ + +- ## Day 2: Large Language Models + - ### Module 1: Fine-tuning + - **Notebook:** [`finetune.ipynb`](<1 - Fine-tuning/finetune.ipynb>) + - ### Module 2: RAG (Retrieval Augmented Generation) + - **Notebook:** [`rag_afternoon.ipynb`](<2 - RAG/rag_afternoon.ipynb>) + +--- + +**Time to play with LLMs !** +Today you will explore two fundamental approaches to customize Large Language Models. In the morning, you will learn how to **fine-tune** a pre-trained model (GPT-2) to adapt its behavior to a specific task. In the afternoon, you will build a complete **RAG** (Retrieval Augmented Generation) system, giving an LLM access to external documents to answer questions with accurate, sourced information. + +> Here's a list of resources that we believe can be useful to follow along (and that we've ourselves used to learn these topics before being able to write the subjects): + +## Module 1 + +- [HuggingFace Transformers Documentation](https://huggingface.co/docs/transformers) +- [GPT-2 Model Documentation](https://huggingface.co/docs/transformers/en/model_doc/gpt2) +- [HuggingFace Training Documentation](https://huggingface.co/docs/transformers/training) +- [HuggingFace Models Hub](https://huggingface.co/models) + +## Module 2 + +- [Sentence-Transformers Documentation](https://www.sbert.net/) +- [ChromaDB Documentation](https://docs.trychroma.com/) +- [Ollama Documentation](https://github.com/ollama/ollama/blob/main/docs/api.md) +- [What is RAG? (IBM)](https://research.ibm.com/blog/retrieval-augmented-generation-RAG) diff --git a/AI/Day03/1 - Regression/images/example_m-function.png b/AI/Day03/1 - Regression/images/example_m-function.png new file mode 100644 index 0000000..35bf6f5 Binary files /dev/null and b/AI/Day03/1 - Regression/images/example_m-function.png differ diff --git a/AI/Day03/1 - Regression/images/example_polynomial_function.png b/AI/Day03/1 - Regression/images/example_polynomial_function.png new file mode 100644 index 0000000..b69819d Binary files /dev/null and b/AI/Day03/1 - Regression/images/example_polynomial_function.png differ diff --git a/AI/Day03/1 - Regression/images/m_function.png b/AI/Day03/1 - Regression/images/m_function.png new file mode 100644 index 0000000..cc6bf95 Binary files /dev/null and b/AI/Day03/1 - Regression/images/m_function.png differ diff --git a/AI/Day03/1 - Regression/images/m_machine_learning.png b/AI/Day03/1 - Regression/images/m_machine_learning.png new file mode 100644 index 0000000..6ca748f Binary files /dev/null and b/AI/Day03/1 - Regression/images/m_machine_learning.png differ diff --git a/AI/Day03/1 - Regression/images/mse_loss_explication.png b/AI/Day03/1 - Regression/images/mse_loss_explication.png new file mode 100644 index 0000000..c4f65b9 Binary files /dev/null and b/AI/Day03/1 - Regression/images/mse_loss_explication.png differ diff --git a/AI/Day03/1 - Regression/images/polynomial_function.png b/AI/Day03/1 - Regression/images/polynomial_function.png new file mode 100644 index 0000000..d5f4340 Binary files /dev/null and b/AI/Day03/1 - Regression/images/polynomial_function.png differ diff --git a/AI/Day03/1 - Regression/linear_regression.ipynb b/AI/Day03/1 - Regression/linear_regression.ipynb new file mode 100644 index 0000000..f619412 --- /dev/null +++ b/AI/Day03/1 - Regression/linear_regression.ipynb @@ -0,0 +1,662 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ~ PoC AI Pool 2025 ~\n", + "- ## Day 2: Understand Machine Learning\n", + " - ### Module 1: Linear Regression\n", + "-----------" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Welcome th the second day of your PoC AI Pool !\n", + "\n", + "*We had to make sure everyone was up to speed on basic python (and ai-related python libs) knowledge before heading into the main topic of this Pool: **machine learning** !*\n", + "\n", + "Yesterday, you learned some very useful skills which we'll put into practice in order to build our very first **machine learning project** :\n", + "\n", + "- python\n", + "- numpy -> (to work with huge numbers and arrays)\n", + "- matplotlib -> (to display graphs and visualise data)\n", + "- pandas -> (to edit and analyse data)\n", + "\n", + "Today we delve deeper into the theory by entering in the world of **machine learning**. This notebook introduce the concept of *Linear Regression* that are the simpliest function to train.\n", + "\n", + "The problem you will encounter today is simply the multiplication by 2.\n", + "Here the theory to understand it better :" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 1.0 The theory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Primary what is machine learning ?`" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + }, + "polynomial_function.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imagine that you have a mathematical (polynomial) function like : \n", + "\n", + "![polynomial_function.png](attachment:polynomial_function.png)" + ] + }, + { + "attachments": { + "example_polynomial_function.png": { + "image/png": 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Vc2VyQ29tbWVudD5TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KwqQnEQAAABxpRE9UAAAAAgAAAAAAAAClAAAAKAAAAKUAAAClAAA3em2vTdgAADdGSURBVHgB7J0HuB1V9bd3/gqCAglIE4hUBUF6kyISEKRGE0HB0CEiRUF6lSIt0gQEQekdAemEjsQgCUovQihBmkCQ0EQUwc93vufguSezy5yzZ86U33qePPdmyp6935k7s9faqwz6z3/FSERABERABERABERABERABEQgA4FBUiQy0NKhIiACIiACIiACIiACIiACCQEpEnoQREAEREAEREAEREAEREAEMhOQIpEZmU4QAREQAREQAREQAREQARGQIqFnQAREQAREQAREQAREQAREIDMBKRKZkekEERABERABERABERABERABKRJ6BkRABERABERABERABERABDITkCKRGZlOEAEREAEREAEREAEREAERkCKhZ0AEREAEREAEREAEREAERCAzASkSmZHpBBEQAREQAREQAREQAREQASkSegZEQAREQAREQAREQAREQAQyE5AikRmZThABERABERABERABERABEZAioWdABERABERABERABERABEQgMwEpEpmR6QQREAEREAEREAEREAEREAEpEnoGREAEREAEREAEREAEREAEMhOQIpEZmU4QAREQAREQAREQAREQARGQIqFnQAREQAREQAREQAREQAREIDMBKRKZkekEERABERABERABERABERABKRJ6BkRABERABERABERABERABDITkCKRGZlOEAEREAEREAEREAEREAERkCKhZ0AEREAEREAEREAEREAERCAzASkSmZHpBBEQAREQAREQAREQAREQASkSegZEQAREQAREQAREQAREQAQyE5AikRmZThABERABERABERABERABEZAioWdABERABERABERABERABEQgMwEpEpmR6QQREAEREAEREAEREAEREAEpEnoGREAEREAEREAEREAEREAEMhOQIpEZmU4QAREQAREQAREQAREQARGQIqFnQAREQAREQAREQAREQAREIDMBKRKZkekEERABERABERABERABERABKRJ6BkRABERABERABERABERABDITkCKRGZlOEAEREAEREAEREAEREAERkCKhZ0AEREAEREAEREAEREAERCAzASkSmZHpBBEQAREQAREQAREQAREQASkSegZEQAREQAREQAREQAREQAQyE5AikRmZThABERABERABERABERABEZAioWdABERABERABERABERABEQgMwEpEpmR6QQREAEREAEREAEREAEREAEpEnoGREAEREAEREAEREAEREAEMhOQIpEZmU4QAREQAREQAREQAREQARGQIqFnQAREQAREQAREQAREQAREIDMBKRKZkcU54dVXXzVXXXWVefzxx81zzz1nXnvtNXPAAQeY4cOHx7lADq1MmTLFXHzxxeZvf/ub2W677cwCCyyQw1XUpAiIgAiIgAiIgAiIQBUI1FKRePPNN83kyZPNCy+8YJ544gnz0UcfmRlmmCH5N+OMM5pPfepTye+LLrqo+dKXvlTofXrxxRfNmDFjzLXXXpt63Z///OdmxIgRqfv6tfHDDz80p59+uvnZz342oAtbbbWV2X333c1nP/vZAdv1HxEQAREQAREQAREQgfoTqI0i8e9//zuZnP/qV78yf/7zn4Pv3Oc//3nzne98J/k311xzBZ/XzYHXXXed2Xfffc3f//536+mf+9znzIQJE6z7+7Hj17/+tTniiCNSL40ScfbZZ5tlllkmdb82ioAIiIAIiIAIiIAI1JNALRSJ8ePHm4MPPtg8++yzPd0lJstbbrllT22knfyvf/3LHHTQQeayyy5L2z1gGxPz+++/f8C2fv/npz/9qTnzzDOd3TjxxBPNyJEjncdopwiIgAiIgAiIgAiIQH0IVFqRYBUCd5szzjgj2h055ZRTosYpvPPOO+b73/+++cMf/hDUx4UWWsjceeedQccWdRBK0AUXXOC9HCsX6667rvc4HSACIiACIiACIiACIlB9ApVWJPbbbz9zySWXRL8L99xzj5lnnnl6bpfgZFY4srhajR49Olm96PniERs46qijgpW1q6++2iy77LIRr66mREAEREAEROB/BPA+wBNh2LBhZujQof/bod9EQAQKJ1BZRYIJ62677ZYLsB/+8Idmr7326qltMjGhRDz//POZ2uHlWLYX49ixY80PfvCDoHHgmsWKyuDBg4OO10EiIAIiIAIiEELgscceMyeddJK5+eabPz78mmuuUYzexzT0iwgUT6CSigTWCCwReQnuObjpdCukdl1//fWTNKmhbXzhC18whx9+uFl11VVDTynsONyzvva1rwWPZ/PNNzfHHHNMYf3ThUSgjgTuu+8+c+CBB5p//vOf5tvf/rbZdtttzWc+85k6DlVjEgEvgT/96U/J30HngaRN33HHHTs36/8iIAIFEaicIvH++++bb33rW5nchfj4LrjggmbOOec0M888s5l++unNjTfeaM2ehGsOKx7dCBmZ+OiHujORNeq4444zK6+8cjeXy3zOP/7xD4NVh3S4KC+kwg0RVkpGjRoVcmhyDIHlX/nKV4KP14HNIMCkmJopr7/+unnvvfeSfx988IH59Kc/nUySZ5ttNjP//PObT37yk80A4hjlcsstN0B5x9Cw9dZbO85o1i7q2UyaNMmQbY9n5hOf+ESzADRotA8++KD55je/mTridkXiP//5jyH9+yuvvGIw6DFfYJV8jjnmMHPPPXfy3UttRBtF4L8EiLvlvYJb+rvvvpt8n3iGmDPONNNMiacF9bMoIyD5H4HKKRKuVKStYfHC2WSTTZL6EXxkmDB3TkyeeuqpJAg6LdMTtSVuuummVnPBP3kIiXG44447gs7ZcMMNk5oSKDdFyO2332723nvvAZMTakGQlSlEzj33XHPIIYeEHGoIGr/11lun4R50sg6qDYGpU6ea2267zZD6GIuiK/Vx+6BZAfvud7+bBO9PN9107bsa8ftf//rXaRRx/nZ33XXXRozfNUjes2SRO/roowccVsYaPAM6qP90ReCtt94yq6yyivXdsemmm5r11lvP/O53v0sMhEwEbcI3F2+Br3/965oM2iA1aDuGLYyk119/vbnrrrsGzI1cGL785S8nBmMyVQ4ZMsR1aCP2VUqRoDDaaqutZvjI2mSnnXYyBGGHCGlZzz//fHPeeed9HMuAaxEWjiWXXDKkiQHH8GGjcFuI8AAee+yxhUy0+fBSv+KKK65I7RoKxiKLLJK6r3MjL+tQqyjjo0aHpFkEWGFAEed543npRVixQ9Fdc801e2mmcueOGzdumlTUUiRMYmXebrvtzKOPPjrNPcXy/Mc//lErE9OQqfaGn/zkJ8k3OuYoqNdEyvgNNtjADBo0KGbTaqsCBO69917zm9/8xlx++eU99RZvlz333DOZE3Uaq3tquGInV0qR8E1i0RKpGN3NEjcrEyyLLrzwwl3dwocffthsvPHGQecWqUQwJpQIVw2L0047zWCpCRX++EKC0fmwk/YWNypJ/QmgQKCU8zy5rILdkEB5JV4g1BWvm2uU6RwMHEx02qXpigSxWlifXW6jTBDyLizafk/0e74EUBizfJuy9gbD4amnnmpwqZTUn8Att9ySBOunGSJ6Gf1KK61kKB2A+1wTpVKKBPUY2rM1dN4wUsH2I1gZi/9GG23k/MC1+lqkEkE8BEHPuCS5JKsiQVu77LJLshzoapd9hx12mNlmm218h2l/xQk88sgjiduca5LX6xBxd6JyfRMU03322Wca5d+lSLz99tuJKyHuY/iDsxI433zz9Yq8NOe//PLL5sc//rGZMGGCs09SJJx4KrUTI9iIESPMAw88kGu/WfXkG9mtETHXzqnxKASIy2NliwyUeQmrXKzC1+m9G8qqMooEbkjEOtikn4XcQuI26DcvRQKr814CQ7HBJx2XkBDLcDeKBO1+9atftfqttu4TqxJ8/AlWktSTwIUXXpisFhQxurXWWsucddZZ5v/+7/+KuFy0a6BgsTpHEB+BwSR/4H3G30ea4PPdqZSlKRK0Cf8bbrhhmmb22GOP3FJkT3OxnDYQ8Mj9PuGEE4KuIEUiCFMlDuK7QZxUEYKLylVXXWUWXXTRIi6naxRIgOx3pOIPjc/rpWsoE7yLbe/1Xtou87mVUSQmTpzo9Len+jKBzkXLiy++mMRt+K5LcBcVuPNUIoghYcWG2IS0IHJbH7tRJGgLNzJqbvhEsRI+QtXcjyvToYcemkxkixwB1eyLmmDEGFeam1KrXVbrSF3ZXgDTZjTBnbD198Z754gjjvBa2IgZIFtd1YSP/kUXXWQIoM4yAZAiUbU7be8vtYvytCB3XpmVCSaBs8wyS+cu/b+iBHDpZnW3SMFgzHurSVIZRQKLFIVobNKvDwh+21gEXYLlniwjeblksAJBOtsTTzwxkwLR6nO3igTn47vuC6hltYhMVgpqaxGv/k+eOSbAZGQqWrAeEow8++yzF33prq6HXz/vJ5fgtkXaaFZcUBJYkegUrFxkWSNWgPdJiOAK9o1vfCPk0FIcwwoE79OTTz45kwLR6ny/vgOt6+tnHAIvvfRSX9yUyegUmjAlzkjVSl4EePcdeeSReTXvbBcjyOqrr+48pk47K6NIDB8+3Dz00EOp7Pn4nnPOOan78tyI3y5p6VxCthlWIvJQIlouTHx0s6xAdPa3F0UidEXm0ksv9bLq7Jf+X14CrAoQpJhFSKtMlhSSIuBHysSY+hEIqYJ5DjvdeWzt92sF0tYf13bqqbgyzXWeC5cQl8TO89L+355jP21/WbaR4pOVm1/+8pddKRCtcUiRaJGo9k9cgAlezSKkfedvDQMDf0O4P+IbTyIU4idD/6bI5lNUXacs49Ox4QR8iXnSWuKZoUbZCiusYFidItaM2hGkH2fuiYIZajjr15w0bVxFbKuEIsHS9uKLL27lwUoFD0DRQgyCyzKIJR73n9h1IlAgyHt8/PHHf5y2tpex96JIcN2QGBGC0bNOPHsZk87NjwBB/IsttljwBUiAQNA/sQEuIbjyzjvvTDKC+T76xBeEvtRd1yxi37Bhw3pS9LvtIx9G3hPtblPdtpXXeaxAEOjKOyiLC5OtP1IkbGSqs53c/hSFDX0eeHZwHXZldOObiZsLirVPmEgSNKsVdB+p8u7ffPPNk5i0kB4S14ArEsqj755j6MKIRVILnxCbUZVVc99YfPsroUg8+eSTSWEq22B+//vfJxqkbX8e26nMu/zyy1ubxv0CdyOqIMYUAtB4kCmoF0t6VSR4SYdkraqqv3YsznVphwBfXtQhQsAvRdSypGTGer/tttt6VydI5VeF4EgsWZ3F00LY9XIM6QjHjBmTFIbspZ28zkVpxEpMnEfohDGkL1IkQiiV+5gs1mSMebj7hcoTTzxhvve973lXJ7SCHkq0fMdlMXShgOISniUuBkWXWmW//e1vnYPHrWqLLbZwHlOXnZVQJFwvFqxu999/f+H3gwDiX/ziF9br4s6U5udsPSFgB1kldt9994Ajsx3SqyLB1dC+SW3rkiq5o7jG0fR9LPPiaugTYgNwUehGmFwSW+RameDvL7R2Szd9iHUOSRD48OAykbdgwCDl8iabbOK1ruXdF1v7H330UVIjwxdbZjvftV2KhItONfbxt4KS6RNWJSl8mTWBCUY43KBcCizKRtHKv2+82h9GgPcLWfF8wvODx0jLvdZ3fPt+DCGUI8CYZRMyRWEoaYJUQpHgg0NQc5rwQiBGoEhB42U1wvYiIriRQJ+YQvVpKrpmFdyrWKp1TWJiKBL0Ky33fXt/FXTdTqO6v+PPvtRSSzkHwEuaFMQzzjij8zjXTnykXYoIhRZ33nlnVxOl2cfHjfFQsM+lHPXS4XXXXTf5cJW9IBtubsRCZBXizd5//31nLYk6KRK4TzBZaRfSaF988cWlXWlq72s3v5OxbJlllrF+W9vb5G+JZ6IboTaFyx0ahfzBBx9U2vJu4JbgHIoY+orOkQCml9ohvGswltkEQ1gexhLb9fq5vRKKhCv6Ho0Pza9I8aU9jf0xw4LCElxWwa2EFHqkhG2ljUxrI5YigbvXGmus4fwIsBzocglL65+2lY/Acsst55wQs1zsW6HyjYpAyRVXXNF6GClgCfqukqBQEPyJYeDqq6+OEuPEqiyrEFVYneHvn8JyWYRJHW5ajM+XJS/2uzdLP2MfS/2Mww8/fJpmyfBFYHodhbin7bff3jo0EjUwQeQn8T8+n3ZrQ//dQTrlyy+/3HpIL4qKtVHtKITAnnvumcS52C5GwU68SnoRViXWWWcdq5s5sRe+Apq9XL9M51ZCkTj77LOTD2UaOJY2yQZTpOD3RlxGmvASpIJiTGHCsdtuuwU3iX80Vr+Wtu1TfGIpEnTQpfSxn3SxaR9H9kmqQwAFniB7m9x1111R4oNIsmBb+auDxYfgPXLls2KYJbNTizsWMfzEe1n5abVVxM9Qt5VWX3jXstI5ePDgZFOTFAlX5qJ+xAW27kmeP1lhTCuuyDUxGHL/+TuZd955ncHVIX2cPHmyc0UDN+KsSm/IdXVM/gR8Cil/W67VhNAe/uhHPzLXXHON9XCesaoVT7UOxrGjEoqEq6BT0ZHxvvzWebzgyQzFZMEnLf9o8tG3P7xFKhLvvfeeU7HDesqSfXv/fOPS/vIRYLUAP+K0oH+shbbJQNaRuBQJXPauvPLKrE2W8vheqvjCgXtBDvxu/H2LBIKhJSTbFm6QWAwZW7s0SZE4+OCDrSsPxA2G+IG3syv77z6XSQxqZHOKKa5ViTqv/MRkWMa2SACD8ZVVqzTBbW3WWWdN25Vpm0vxpaFJkyb1rPBm6lCfDq6EIoFP6P7775+K6PHHHzdMoIsSAjxtS2JLL710ErwTuy9YLX2B2wRXktqOiXqnFKlIcG2flo7vvM/HvnMM+n/5CLz55pvJRIckAO11TGImGnCljI2psPSLLooY7lmuoL3QvvEexKceK35Z0w66jEKtcRL7Mnr06CR/e2tb62eTFAlXzBmZ07DK10lYldt7771Th4RiSWro2OL6tvL3hBuVjF6xqRfTHkkueKZQQNtdjHD3ts0ns/aM9y2u4zbBjbW1mmo7pg7bK6FIuF4wRS4d4d9MTnybCwJxCFg48hCbKwl+4vxzxR0UrUiQ9nannXayYiB2I4urlrWhku7AGsIEkeeFjxAvEqwfVXE/6QYrSgUfXtK8xvrwUmNgiSWWsHanaorE22+/nQSgP/PMM0l1aowgvoBA6+A9O0iJSXKGslmtWbHEvZFYhnbBAEJKYd5lFIOySZMUCdxqbCkmWdWlYFadhHvfPuFrH1teGf98qULrmrKcOcwbb7yRIOa7NGTIkORfrHd3+70rw++4xzI2khVkSUXu6zsB+wTu20SKhI1MH7a7AvT+8pe/FNYjX6YHfMbJnJKXEKCJJYo8xquttlri3xkyOcWHj1UCm8SMkeAavgkgMS3EtpRVULxQhroRlAc+8mmZeQi+YuzUPuAfRd2wtLkKKXXTh7qc46tXkUd2tLzYsWJDrRVbvEde1yW+bO21186r+a7aJTMP7/RHHnkkUT5JJIEhJOQDjyWRFWqb1CnY2mXtrNsEhYktValtEivmKq19V+II3PDIQFcmwQUMw+I777zTVbemTp1qVdjwqvjiF7+YfJ/4VjH2smeB6wpChJN8btxcosj5aYQhdd1EJVYkXBZ1rHtZ80h3S8sX9FzWAlkU18FdwCaxFQmuQyBTp9Wx/frjx483Q4cObd9Uit+ffvrpwidevLCpUu6yvpcCTsGdoNoo2Z9sEnOJ2naNGNtJWcpKZppyGaN9XxvnnHOOWWuttXyHVWK/z22yToqEqzovLjllj4fJ8kDZMlTRBpPZkLiaLNdrP9ZlVS5jYTqCwHEnLUpYaSbLEUq8jF7/o+4zdFVtxfx/I8v+W+kUCZYamczNMMMMybI8SoLLVSZPS0UnTnKfkw3JJlTNDFkhsJ2f13ZfJqULLrggSdsa8/pMAJkI2iRrRVJbO7G3499/1FFHxW42qL0yWo+DOp7DQbiHkX3MNfmuygTZ5YedA7ppmsRtiAl2UQaXaToQcQNuUQQa26TomDlbP2Jsd+XCL9KAFmMsvjYocEmhyzTJ2xXWNTGnRhW1qsoipBxdYIEF+tIdAt1RrJibSUxSHNil0OHeTZa6JkipFIm0irlodbjypGWH4QblMQm23XjSupJbOk3KnDP4hBNOMCeddFJat5NteaTQHTdunLO+By5grvSh1s7mvMOnAOV5+TpN+HrlRABbZzGuzjbLqrh39jOLIoGr26uvvup0gcL9ACstwacuRau9H/fff39qIob2Y6rwOxnpcB20SZ1cCXD1scXj1WmcU6ZMmSY7V/v9xZCY52othiMMSGlC5qwddtghbVdftrG6iVtsvyRvpa5f48p6XV+NI9q74oornHWQsl6zzMeXSpFwLeXaIBZZkM6VuhDXASykZRTqNrB0bJM8AsoILF1yySVtl0x8ox977LGeCgpZG+9hR0hWmR6a956K77grcN7bQA0OYDWCeAIm4DapUnwEk338sH2yyy67JLFMWP0OOeQQ6+GtHOhYJ4m9IGaAFNEuqYtPPfEUNqNSXpl9XFzz2uezPBOvN9tss+V1+ULbdcVA4laT93fCFcCfV5B3t4CJwcO4WnSsVau/uGhecsklrf829ifv53PPPdc6fp5b3rl1WAW2DrJtR6kUCVeqx7Y+D/iVlQCqGGK9ydvn3rXUTB73o48+ekDfyvIfUuqR+comeS2TDxs2bEBa0M7rF+mW1nlt2/8JAGUS2y/BysyEqMniSvfc4hKjcnarrSJ+uoKEKaxHDFNL8SaTE+8am7QUifb9rGLgwpimUDD5dhkS2tsp+++uwNg6TXJeeeUVs/LKK1tvByvM+PbXQVCgbfn++Tsghi9PIdWwLf1yGV1wXQbNPDnR9ogRI5wuy3lfvwztP/nkk96kOjEqZ5dhrKF9qLwi0T5QXENWWWWV5INM5gH+zTPPPNHSUbqWmvOoaN0+tl5+dxVNgRluD3mILzCybP6nMMAajk9sXmk5XZxJe2mrmO46r077nnvuOUMhKJfwzMIJq09VhExFKAAtFwr6jvGBf52Ko899IU2RaHFAoWCVlgQVCM8UCgYB/XUQV4HCkSNHOoPzqzJ+rM240I4ZM8baZZ4ZChCSzhKrJz/5R4pLsl9NN910SdrpueeeO8kOV+YgWdc9ZTWduJg8xRWfgdvTqFGj8rx85rbHjh1rSDTRDznllFMMvJoquNnznvHND1CMW4ahJrAqlSLheqH0cjOojsqHFMWClJv8m2WWWTI36eofk3VXZqTMF4t4Ajnlsf6nSZ6ZBXzB6Xwg+FCUTUjrRjyMy7XG12fcWWz+zbZzy2j9svU1j+34SuPeaHNdaV2TIH4sY1UUXBNefvnlJF7BlZjB5cLjUiRaTJ5//vlE0cL9ZdCgQa3Nlf7pc/cpe3AjyiTPNi4PvFtQAPgOoVSiABD3wD5XXvpubyBFCvFvRwkvk6D4klTBJkVMyFyrXBSg3XjjjW3d69t2YhBdiqavYxRry/p949m55557Gpu5CSMjtV1aRhobY+qhUGS0SVIqRcLlOhT7pmCpW2ONNcwGG2yQLCH7fNn4w+u0HLb3ieVR/CnLKK70dmuuuaY1gLzXsdxxxx1m2223tTaDclfmehLWjgfuQJnAv5fJAXEov3Nkm0FxoYBYXSZ9gYg+PgxXDpSI9grZH+9s+4XVCnxT61o4qTVUCltefvnlrf8O+BmiSAw4oSb/8eVtP+CAA8yOO+5YutHyd48l1xUkXkSniePDxa1Mfzuwca04oHix0pKXMDlceOGFrc3nHehtvXABOxg7fLGu49KLe5fN+IXBkdXU+eabr4Cele8SH3zwgaHgMKtBLkHZuvXWW0unsLv6HGNfqRQJXrRk5ShauPnbbLNN8kKzlTNnuZkVCZuUNQsR/XVZN3HjwcUoD3nhhRfM6quv7mwaV5amTJ4JQMclh5f3Sy+9ZHg5sTqGMuuq5usEWIOdWM9xH+CnS5r0kkZZsgVc80Ffb731XKhquc8XtE5qbpTRMokv1qHovpZt1dOV3KKITIg+RQarfZ3qdbieN1b8SJ88ceLE5PvECjErZiigxHHhLtdEoSTBrrvuGlTL5Morr3RmIKsrv1IpEkCeMGFCUvjEZ5nM64bgnkTayc4VCp8iwWoFgbJlFFdsB25PfFzyEJ+1h2tirbcpb3n0SW2WiwCWMJaCQ7KQUKF9mWWWKdcAcuoN70G4pEldUrmmjc21zWeYyKOwpqs/IftYcS3TKknZVoG5ZzYXHVYfUTTyFFf8oGLW8iRfjbbfeOONxFMgxN2wbEp6kYRLp0gweCy1LCnecMMNhnzyRQsxFZdddtkAZcKnSNBHlB8C3comrtgONG2yOuUlvsxNWIQWXHDBvC6vdktMIKRWRKv71Bxh1a8pYkufnGdMU9nZ+rKlFFlTKJQVFu0yrR7lXSU6lEvruGOPPdYQh5AmeWe+8a1wlT3mJo2ZtsUjQGFkXLN9K+Vcscyu7fGI2FsqpSLR3t13333X4PuKNbJI6bSUhygSlEyfd955i+xm0LVcaXXz9ivmD5FYCZs0dSnQxqMJ21lCJxDfZonsZFDGzCmdfczj/2Rf6izaWOVA814ZYRV0pTy9+uqrDdV3yyTE1pG9JWTFrYh+ly3rDlbctJTFsMjbXfjss882hx12mBV7GZ8na2e1IyoBgtnJjBXyd4t7OO/lMsUeRYUR0FjpFQnGgIsMMQyu1Jj4U1LxkfLx+PK9/vrrhsq3WTMTtJh1Vs0l24rPcl62lzRjIV0Z2apsQu0LUlDmJb5ieKSmpLhYXYUXEYHWU6dOTTKHYREs46pVUfx5Hvfbbz9DEaoQwdUQ94MmCgrX6aefntSA4R3IJJn6AU2JKeq85xhqXDEQt912W1Lxu/O8fv/fVSchVt/I/OSa9LCfYFGs7GUSjAm2OhF5umHh9YDfvy24mHgsYjbrPjlkXkOcIko6Y8Z1dMiQIWV6RArviytup7MzxJ/yjm5q/EiLRyUUCTqbZp1rDcJVfZIPMEtU9957b5KVwKWMtNqzKQSuNHGcW8a0X2+99ZZZaqmlWkOb5mfeFk7yoZORyCZ5KzK268baTiYZUhjyQSKAmtSeL774YvJyptAfy+ftwgedQlPcE17cvLT5h/vZ7LPP3n5o7X5HucdfPDR7TZOViNrd/AgDuv322xN/ZVtTd999dymzyrz55ptmk0028aY1to0rbTsBsDvssENiJOI9woSX1Q9W8KlDwj+Udn5iuMDI1hn3l9Zu0duYhLkKueaVjIMCrS6X3t133z1J9Vk0j9jXQ1HgG8T3qfWP7xOphidPnpz6TGLsIq6SDE2kj+b7NOeccybfrDorVswVDz30UIOLZIiwYnbqqafmmlUspB9lOKYyigQZOXCHSBOUjC233DJt1zTbmPgRrPjggw8mE0D+yMjKwIoGxXt4QfN7mvjS0/JCv++++0plMfRlDeFFTmGjvMTnB4+PLL6wZZJJkyaZ6667LlEKeInOOuusyQsVJYAPG6tcBH7yInZZAbOOiQJ+LKdynboJzHBzs1kAO8fb5MC1Thb6//8nwN8kMV02QUGdY445bLv7uh1jA0aVNMGaiWGBDG5M9lEIcK3FWm9LOsKqXtlWF9LG5tvmq2JP0gHb99jXtm2/bzWC8zA4ljWTHlmESDGK+w2FBvk+tb5R7CPlOM8NCkOIf7+NU+d24rOYaK+44oqduyr/f5R9/p5Y9QwR3JmOP/74xq9EtFhVRpHgptnSlBY1GWXFgRebS8q2vM5kl1oRNsFPdO2117bt7nm7L71emQr+4Epy5JFHTuOX3jOEDA2gjGIRWWKJJTKcVe5D+Zug8nuo2FYEQ8/XcfUk4LMid8a1VZ0Cxa9sLoBY011KVVXGTlIVl0KURwD9pZde6iweW0S2qG7vD0oCxqaYCkLWvpDpkdiSuqxOoHRRyySUKW72eFk02UW585mpjCKBb/AJJ5zQ2f/k/0XlD2e586qrrkrtQ2tj2SypvHioU2ATrGQuRcN2Xuh2VxpL2jjnnHOSVaDQ9vI8bvz48Uk9gzyvEdI2KxKkEp5rrrlCDi/1MdQ8IFg6VMr0PIT2WccVQ8DnJkkO/Dqt5rlSkxLvQNHCqgueAVh3bRLbxYjq4hSida2M5r1KbxtryHbqbIW6hoa01+0xKLncm6oL33zS/Yd6FuA1sOeee1Z92NH7XxlFguwluDClCVbkLbbYIm1X1G2UPccnziUERJLtoSyCq9XIkSOt3WFpebXVVrPu73WHL9MKVkZiBsog+OryESmD5B27kvcY+WCTEcxWnTnt+lgKV1lllbRd2iYCiWsrRiObkB52hhlmsO2u3HZXxjusoq6MQ1UZLHFTyy+/vLW7sQOuXZ4NdAI3Ktyayhg864t3tELMYQecfN4ZOVw2apOsdhFfGyp5Z7gM7UcZj6uMIuFa1qYC7HbbbZc730suuSTJOOO7ENZkCtSVQQhAdGVlynsi71sRuf7665P0iGVgxZLx2LFjy9CVJNCvqhYf4o6w8oRazrAiX3TRRaVL3VmKB0Gd+JjAiSeemKRZ/HhDxy8kNyhjQHFHN4P/S4Yqm882me7IeFd1wZ2UTIsuiZWG1fctpA9ldqssW00SYgmJ0aiaEFRNNklbzFLaeIoyVqdduwrbKqNIuIJ28W/jwchbCJhbaaWVvJeJvRzrvaDjAF+mE3xwXRYhR9NBu3wv7zIpXZtuummS3StoYDkfVFTcT+xhZA2qRomglgiWR4kIuAjgIoernE3IRFMnGT58uHnooYdSh7T00kuba6+9NnVf1TZSG8RVOZggX+pI9aIksvJBlp3OLHrtrHgHEbNRVt9/n5tw+1jy/p1YPpLWVE2oVI17Ukj2ztbYqu4d0BpHnj8ro0jcc889ZrPNNktlweQ+iwtFaiOBG7G0+qpt80dG7YAyBONQHdyVh5+PER+lvARFBX9Km5SpiB+ZUFh16qeQeo8UqbijleH5CWHBx5n7TFYd28QnrR2Wx3GtK8vqXVofta08BAhwdFkR66ZIDBs2zJq1qaoTubSnyVcYjnN6iT0kBSpuYiT+cEkegd2u62XdR3rxVVddNetp0Y8n6QwxOkOHDo3edh4N4mbLPIi5jqs4btq1zzrrLEOtCImbQGUUCSLrebGmCVZNAu2KEB5EXko+YUJahj9630QeC0yeGYKIKSG2xCbU+CiLPyqB9N24E/H8wZDCf+TbxqJFikFeYCyj8pN87gR0vfbaa4aUvPw+/fTTG6qO80Km+i2xIqSArIKw8oDSQLBkN8oXyisTiLrXzqjCvaxKH8lUhCumTeqmSPjqFtUluJxinRRC8wm1oLImoKCWBs8NKxouybuKtuvaoftwAyOGzBUobmsLY02rYO9MM82UpBhufZ/4VsGJ1PikQaV9frKf7xnfJ4rx8n3iH+eXXfAeYQWH7xTvDNdKVNpY+KZj5Ap5LtPOb9q2yigS5Ed2TbK6ecl0c7OZGLIC4nswN9poI29gdjfXz3qOL9Ud+ahdla+zXq/z+IMPPthQKTJNyhawxb1dZ511rFZAxsDyNznfURxYPVhkkUWSl23a+Oq2DT4oW1jusqw8pHEgbof7z0dp5plnNrPMMkvyc/DgwUlOdD5gvbgypF1T26pNAJcE24SwSGNSURQxMrjkpptuqo1LoCvVbYsBaVmxEIcanlBQ8CBgbuASVndIUU3xtbILVnVWA2zC3wGT/ZZhi287SgRGqyYIWZiYb/i8RnwsqHqOy3fr+8Q3in98n/hHzbE6JXbw8fDtr4wiwUBcFpq805i2gwwtoU4BPVfq1fY28/rd19e8YxSoH8BLOk34Y73wwgvTdvVtG7mkR40aNSCnNIGNpN0jbeCMM87Yt77188KsoOAi53MPiNVHPohkE+MZoQASH8SquHrFYqB2BhJwJUOok6tPa9Q+RYJga95NdRAm+8So+QSXTzIv+eIYKBxKkdqQ2gBVyxbXmQqfwnlUTt94440b6ybKag3JGGBTlGBQ5vtEFXBW2KsYeB6LVaUUCQqh3HXXXalj32effcwuu+ySui/2RpYCqYDte0kxGWISPc8888TuQnB75557riGrlU3yruDJH5qNU1FB8rax27azpPvUU08ZVsEWX3zxpPK57dgmbMctC0Xq0Ucf7dtw+VvCtXH11VdP/vayujj0reO6cDQCo0ePNrfccktqe01UJIgZyVLoMRVcSTYyEeSbaqvk3d5NYsiIZ0tTJnAbJVX8mWee2X6K9feq1uNgnFSupqL1wgsvbB1fU3b44qeK4MBch+8Tdblc3jNF9KXoa1RKkejUxNthFZ0Oz5VFqr1fxEmQ2jLtpdd+XF6/H3fccUlKO1v7BLHnpehMmTLFrLDCCrZLJ6kcR4wYYd2vHeUggDtBEVnRsoyW52r99ddPVolwMRs0aFCW03VsBQm4svtglc2SiaUKw3d97+j/ZZddllhDqzCWkD6GplenLazBrGDwk5UbJtW8pyhoGSqsbvB91EpnKLFyHvfII48YXMnLJLyPKLSIOx5xFqHueGUaQ5a+VEqRGDduXLJcmTbAoi1SWFBYTgzJlU+cwA477JDW7dy3udwBuDjZpfBHz0N8yhb307d8n0e/1GY2AhtuuGFfVyN8veWlTQIEJgZDhgzxHa79FSXA6qCtAi1+4Lhp1knINsRqcmeMGelQSWCRZ5KMfnBk5RPDmy/+MEbfmHiiqCkOKwbN/rZRpkKyaSRYTcfNjrowvpopaedXYVulFAk+InxMbELxsyIzCviqNrf3k0l1P5a7SF2Gm45NGENeQWak7LMtMRet+NnGr+1+Aq40lP6ziz2C1IQ77bRTkmWk2CvrankSYHJJjJxNWJWyxWLZzqnCdgxWTzzxRGKwajHAhaKuK3ChWRF7uXdkaDrttNNqbyXuhVGVzkXZxoW7CsIKBcbdMmT0jMmrUooEA3cF7/Ih4YNSpLgyibT3g2w/ZFwoOnuCK0Cd/pF9Jy8r7nrrrZekX2vn0Pod6zHBUZLyE3D9zZWx91iAcPsgpa6kHgSIs2ICbRPer2QxklSfwBFHHJHEOeQxkm222cYcdNBBUiLygNunNjFWYrSskhxzzDHJCkWV+uzqa+UUCSYIBFanCXl/yfRSpFAXgOAa25J7e1923XXXJKd1+7a8f3f5FXPtvFYkfPERY8aMsRYYzJuJ2s9GgFzcKIVZBMsLMQykeCXTFS4ExAkRyE6yAgLZsbDynLzwwguJ1bXXlLLt/Ss6Zqr92vo9PgFy3KMs2KSuKxK28dZ5O+8I6vlQRCymYLjCgCWpFwG+Jcz7srjE4Q6LgYz4UFKPE8PA96lV94m6GtTSaNV94htILEbIPC+Ubp3q3lROkXj77betlkYyOvXDB81Xq6H9waICNwFiRYmrWjOW27wK+fHSprS8TUgjSpEbSTUIENjoy8feGsmBBx6YxARlTTDAS/zJJ580EydONHfffXdPriqk5MPoIKkPAe6prRhXWer21Id2f0fy4YcfJnEgp59+es8dWXbZZQ1+9C5FtOeLqIG+EvAlJmjvHAleWPXqxg2eyT/fQYrdEZOVRXlp7wO/P/PMM7WJ0amcIsENSKvWfPLJJydR8uwvWgiKY6IVEniNhXbs2LFJ2rYi+umyJm+xxRbmyCOPjN4NtHZy/9u09zoGRkaHWLIGqRRK2kVWsGyCgnzAAQcYPtwxBMWClLMoFqT9DPn7al2X2CCyuEjqQ8BVE+eUU04xw4cPr89gNZKEAFWJqUzdjWAow+WFiWNWo0Y319M5/SPAKjdJbcj8ZRNWIfbYY4/kebAdk2U78UuTJ09OFAsMo8zrsohWJLLQyunYhx9++ONiZlSvpMJwP4WAZiYvIUKgDdWBi8oYgWX2sMMOGzCxR6FhcsayXmzxpQutau7u2Jyq1h5WQl6YN954Y+KKhDVm6NChSaVvMpjlbfF7+eWXk2cWlwfX6giB/DzzRcdLVe1+Vq2/uBsce+yx0/jPy42tancyW3+ZcO21117Ov/n2FjFokCGH5wJlQtIcAhierrjiCkPiHVxm+RaQ5IZUrLg/5Znq95133jEkC0Ch8CkVZShWHPOpqOSKREwAMds644wzzFFHHRXUZNHp5/AxZimNFysKzLzzzpvLHxUVRanmbVuNAA4T0bqlLgy66TooGgGUGJR3XF14gfNc89EgAxmWp7wSCEQbgBrqmgD3furUqUmczdxzz23mmGOOrtvSidUggPUXd0eKzWHMaBcMGFS+5yexXHKZbaej3/tBgLgN3MYpHMj7iqrXFA/kG4URN6/aXf0YK9eUIhGRPK4YpJ8MdcEgIJUl+cGDB0fsRf+aQonYbLPNrH7M9IyXvbKr9O8e6coiIAIiUGUCrIwyQSMFLjWQilrZrzIz9V0E8iQgRSIyXXzmyOIUKrhfXHfddUlmm9BzyngcytNWW23lXImg38RkEJshEQEREAEREAEREAERqDYBKRI53D/S0O6///7BLTMBr1oe5PbBXXjhhYZMPT7B/eS+++6rvNLkG6f2i4AIiIAIiIAIiEATCEiRyOkuE1BMAbpQIRPV8ssvH3p4KY4jDgKF6ZprrgnqD8oSSpNEBERABERABERABESg+gSkSOR0D999992kIBcVWUNkhRVWMFdeeWXIoaU4ZtKkSUk60GeffTaoP4yPVH55Zk0I6ogOEgEREAEREAEREAERiEJAikQUjOmNuGo4pJ1BnEEVMpCQfnPnnXdOG0LqNrIUsGox11xzpe7XRhEQAREQAREQAREQgeoRkCKR8z0jVd3WW28ddJVx48aZ+eefP+jYfh108803G+p2hApxESgeSskXSkzHiYAIiIAIiIAIiEA1CEiRKOA+URxrn3328V7p1ltvTfJhew/s0wGvvfZakpHKVSOis2vXX3+9WXLJJTs36/8iIAIiIAIiIAIiIAIVJyBFoqAb+MADD5hRo0Y506M+9NBDpS6kNX78+GQMIchYiTjvvPPMiiuuGHK4jhEBERABERABERABEagYASkSBd4wUp+OHDky9YpMvKmEWGZ5+umnzdprr+3tIrUxzjrrrNK7aXkHogNEQAREQAREQAREQASsBKRIWNHks+O2224z22+//TSNU+X6/PPPn2Z72TYst9xyScl3W7822mgjM2bMGDPTTDPZDtF2ERABERABERABERCBGhCQItGHm3jLLbeY0aNHD7jy7bffbhZZZJEB28r4n0svvdTsu+++03Rt1VVXTeJAll122Wn2aYMIiIAIiIAIiIAIiED9CEiR6NM9nTJliiEDErLBBhuY2WabrU89yX7ZCRMmmIkTJyY1IRZaaCGz2GKLGX5KREAEREAEREAEREAEmkNAikRz7rVGKgIiIAIiIAIiIAIiIALRCEiRiIZSDYmACIiACIiACIiACIhAcwhIkWjOvdZIRUAEREAEREAEREAERCAaASkS0VCqIREQAREQAREQAREQARFoDgEpEs251xqpCIiACIiACIiACIiACEQjIEUiGko1JAIiIAIiIAIiIAIiIALNISBFojn3WiMVAREQAREQAREQAREQgWgEpEhEQ6mGREAEREAEREAEREAERKA5BKRINOdea6QiIAIiIAIiIAIiIAIiEI2AFIloKNWQCIiACIiACIiACIiACDSHgBSJ5txrjVQEREAEREAEREAEREAEohGQIhENpRoSAREQAREQAREQAREQgeYQkCLRnHutkYqACIiACIiACIiACIhANAJSJKKhVEMiIAIiIAIiIAIiIAIi0BwCUiSac681UhEQAREQAREQAREQARGIRkCKRDSUakgEREAEREAEREAEREAEmkNAikRz7rVGKgIiIAIiIAIiIAIiIALRCEiRiIZSDYmACIiACIiACIiACIhAcwhIkWjOvdZIRUAEREAEREAEREAERCAaASkS0VCqIREQAREQAREQAREQARFoDgEpEs251xqpCIiACIiACIiACIiACEQjIEUiGko1JAIiIAIiIAIiIAIiIALNISBFojn3WiMVAREQAREQAREQAREQgWgEpEhEQ6mGREAEREAEREAEREAERKA5BKRINOdea6QiIAIiIAIiIAIiIAIiEI2AFIloKNWQCIiACIiACIiACIiACDSHgBSJ5txrjVQEREAEREAEREAEREAEohGQIhENpRoSAREQAREQAREQAREQgeYQkCLRnHutkYqACIiACIiACIiACIhANAJSJKKhVEMiIAIiIAIiIAIiIAIi0BwCUiSac681UhEQAREQAREQAREQARGIRkCKRDSUakgEREAEREAEREAEREAEmkNAikRz7rVGKgIiIAIiIAIiIAIiIALRCEiRiIZSDYmACIiACIiACIiACIhAcwhIkWjOvdZIRUAEREAEREAEREAERCAaASkS0VCqIREQAREQAREQAREQARFoDgEpEs251xqpCIiACIiACIiACIiACEQjIEUiGko1JAIiIAIiIAIiIAIiIALNISBFojn3WiMVAREQAREQAREQAREQgWgEpEhEQ6mGREAEREAEREAEREAERKA5BKRINOdea6QiIAIiIAIiIAIiIAIiEI2AFIloKNWQCIiACIiACIiACIiACDSHgBSJ5txrjVQEREAEREAEREAEREAEohGQIhENpRoSAREQAREQAREQAREQgeYQkCLRnHutkYqACIiACIiACIiACIhANAJSJKKhVEMiIAIiIAIiIAIiIAIi0BwCUiSac681UhEQAREQAREQAREQARGIRkCKRDSUakgEREAEREAEREAEREAEmkNAikRz7rVGKgIiIAIiIAIiIAIiIALRCEiRiIZSDYmACIiACIiACIiACIhAcwj8PwAAAP//4F7jjwAAN3lJREFU7Z0J3B3T3cdPigq111axhTRqq51YGpQgqS2WWhNraq29IdYEEdJSDYJWUFtp1FI0IqjataUEaWitia3R2lK1FO/7He88783NzJnlnpk5c+/v//k8n/vcWc6c8525Z87/nP/S7Yv/FSMRAREQAREQAREQAREQAREQgQwEukmRyEBLh4qACIiACIiACIiACIiACAQEpEjoQRABERABERABERABERABEchMQIpEZmQ6QQREQAREQAREQAREQAREQIqEngEREAEREAEREAEREAEREIHMBKRIZEamE0RABERABERABERABERABKRI6BkQAREQAREQAREQAREQARHITECKRGZkOkEEREAEREAEREAEREAERECKhJ4BERABERABERABERABERCBzASkSGRGphNEQAREQAREQAREQAREQASkSOgZEAEREAEREAEREAEREAERyExAikRmZDpBBERABERABERABERABERAioSeAREQAREQAREQAREQAREQgcwEpEhkRqYTREAEREAEREAEREAEREAEpEjoGRABERABERABERABERABEchMQIpEZmQ6QQREQAREQAREQAREQAREQIqEngEREAEREAEREAEREAEREIHMBKRIZEamE0RABERABERABERABERABKRI6BkQAREQAREQAREQAREQARHITECKRGZkOkEEREAEREAEREAEREAERECKhJ4BERABERABERABERABERCBzASkSGRGphNEQAREQAREQAREQAREQASkSOgZEAEREAEREAEREAEREAERyExAikRmZDpBBERABERABERABERABERAioSeAREQAREQAREQAREQAREQgcwEpEhkRqYTREAEREAEREAEREAEREAEpEjoGRABERABERABERABERABEchMQIpEZmQ6QQREQAREQAREQAREQAREQIqEngEREAEREAEREAEREAEREIHMBKRIZEamE0RABERABERABERABERABKRI6BkQAREQAREQAREQAREQARHITECKRGZkOkEEREAEREAEREAEREAERECKhJ4BERABERABERABERABERCBzASkSGRGphNEQAREQAREQAREQAREQASkSOgZEAEREAEREAEREAEREAERyExAikRmZDpBBERABERABERABERABERAioSeAREQAREQAREQAREQAREQgcwEpEhkRqYTREAEREAEREAEREAEREAEpEjoGRABERABERABERABERABEchMQIpEZmQ6QQREQAREQAREQAREQAREQIqEngEREAEREAEREAEREAEREIHMBKRIZEamE0RABERABERABERABERABKRI6BkQAREQAREQAREQAREQARHITECKRGZkOkEEREAEREAEREAEREAERECKhJ4BERABERABERABERABERCBzASkSGRGphNEQAREQAREQAREQAREQASkSOgZEAEREAEREAEREAEREAERyExAikRmZDpBBERABERABERABERABERAioSeAREQAREQAREQAREQAREQgcwEpEhkRqYTREAEREAEREAEREAEREAEpEjoGRABERABERABERABERABEchMQIpEZmQ6QQREQAREQAREQAREQAREQIqEngEREAEREAEREAEREAEREIHMBKRIZEamE0RABERABERABERABERABKRI6BkQAREQAREQAREQAREQARHITECKRGZkOkEEREAEREAEREAEREAERECKhJ4BERABERABERABERABERCBzASkSGRGphNEQAREQAREQAREQAREQASkSFT0DLz11lvm5ptvNlOmTDEvv/yy+cc//mFOPPFEs/3221dUo2Iu+/HHH5ubbrrJPP7442aXXXYxffr0KeZCKlUEREAEREAEREAERKBUAm2pSLz77rvmpZdeMtOmTTNTp041n3/+uenevXvwN88885i55547+H+llVYyK6+8cqnAp0+fbs455xzz29/+NvK6559/vhk4cGDkvrptvOuuu8ypp55q3njjja6qb7rppmbYsGGlc++qgP4RAREQAREQAREQARFwQqBtFIn//ve/weD85z//ufnrX/+aGs6yyy5rvv/97wd/SyyxROrz8hx42223meOPP978+9//jj39G9/4hnn00Udj99dlx5NPPml22GGH2Or++Mc/DpjHHqAdIiACIiACIiACIiACXhNoC0XiwQcfNKeccop58cUXW4J95plnmkGDBrVURtTJn3zyiTn55JPNDTfcELV7lm1f//rXzRNPPDHLtjp+YTViyJAh1qoPHjzYDB8+3MwxxxzW47RTBERABERABERABETAPwK1ViRYhRg9erS59NJLnZG94IILnPopfPDBB+YHP/iBefjhh1PVcYUVVjC///3vUx3r80Gsvhx++OGJVUTZQMmSiIAIiIAIiIAIiIAIfEkAs/znnnvOYOHBWPL9998Pdiy33HKGseIaa6xh5pxzzspx1VqROOGEE8yvfvUr5xAfeeQRs9RSS7Vc7owZM4IVjiymVu0ysL7nnnvM/vvvn4rhiBEjzL777pvqWB0kAiIgAiIgAmUQeOedd8ydd95pvvnNb5p11123jEvqGh1OAOXhj3/8oxk/fryZMGGC1RQeZQKf0379+plu3bpVRq62isQtt9xijjzyyELA/fCHPzTHHXdcS2UTiQkzqVdffTVTOZhpLbPMMpnO8fFglKgsHS+RndZZZx0fm6I6iYAIiIAIdBABoiiOHTvWXHHFFV2tLsr0uesC+qfjCRCE56yzzpolQE0aKJdddlmgTKQ5tohjaqlI4Aux+eabF8EjKHOrrbYyv/jFL3KXT2jX/v37m3/+85+py2DG4/TTTzcbbbRR6nN8P/CAAw4wd999d6pqollPnDjRfPWrX011vA4SARGYncDbb79tjjnmGPP8888HLxbMKtthYmL2lmqLCBRDgEkwArA0+1xuttlm5pe//GUxF1WpHU/g4osvNmeffXYuDlVbstROkfjoo4/MjjvumCky09e+9jXTs2dPs/jii5v5558/GKz+7ne/i10yWmuttQwrHnmEiEw777xz6voRNeonP/mJ2WCDDfJcLvKczz77zLz33nvmP//5T7DctdBCC5l555038tgiN9IhE+7VFqWq8foMgIpaZWq8jv6vFwF8oVDKeZ5mzpxpPvzwQ0M/gNI533zzmQUXXNAsv/zyhtDOnS7HHnusufHGG7swbL311oZIdp0kvvR/ncS8XdpKP7PbbruZv/3tb7M1qVmR4L3GpCHhzXnf8p5dbLHFzJJLLhmMM2YrQBtEIIIAz9Fpp50WmDJF7E616ZBDDjGY+lcltVMkWClgidEmhB0l+Rm2ZoR0Zba/2SGFjoLZuuZZB8oltwR2kVmFAQ+a4b333pvq1O9973tBTgmUmzxCp0c7XnjhhSBvBrkzaE9Um1CmUFp69OhhVlxxRdOrV6+ACysBDMSKEmz98H9Iq0zcf//9BkciSecSIIkhJn633367+cMf/pB6ZW+11VYLlPiddtopeKl3IsHvfOc7s5hTssJZhB+ZD2zr0P/5wEl1SE9g7733Ng888EDkCbwrmTGmb5o0aZJ1snD99dc32223XWCZgHIhEYEoAs8++6w5+OCDZ+mzo45L2jZ06FBz2GGHJR1W2P5aKRLMNG288cZW+7EsmhlhWa+66qpguTL0ZeDFS4bp1VdfPTP0UaNGmUsuuSTVeQx2yKXQrOCkOZmEexdeeGFL5leN10HBYEVkww03NFtuuaVzxSKLvwhcfvrTnzZWT/93CAGUzl//+tctzcyACqWZmfl99tkn1++rrrhZqWlOsNmOikTd+r+6Pk+dVm+sFBg/uBZyR+23335aMXUNtsblMcmN/w3m7K0IecdwtOZdx+RwVVIrReK+++4LgMXBYkYSZ5U8eQmYxf/iiy+C2fq48m3bJ0+eHMxA2I4J97WiRODJzzJY2hn+8JpZPrEPPeKII5zaVoc222nqwWpQ84AozXk6pp4EyDnys5/9zDzzzDNOG8CsIOGcMTXoBCE63DbbbDNLU9tNkahr/zfLTdEX7wjwPmU1j1WuIoQB37hx48yqq65aRPEqs0YE3nzzzSCYT9zKl60pTJIx2YuZXZ8+fZxEF7VdL+2+WikSmCLhkBsnLOHz4ixbMGnadtttrUudYZ1aUSJYPSHxXlmClstymauM31dffXWqnBGtOruXxUfXaY0AkVFOPfXUIMRdayXFn80LHJ+BpZdeOv6gNtlz8803m6OOOmqW1tgUiU8//dQ89NBDgSkm/iYoIVkirc1yoRK+1L3/KwGRLpGTANYBrPIXLVVH1ym6fSrfToBJUnxBs04E0y+zsrX22mt7ucpeG0UCMyR8HeIE+8WqErml8dug3gMHDgwcq/OYMyWtxsRxaXU7GjBh8NCAWxWW8/bYYw/z6KOPJhaFwvitb30r8TgdUE8Cjz/+eBAeOWuHmqe1KBN33HGHIWt8neT1118PBvp///vfA2WePo4/TBGjhLCBzck5oxQJfKlYVkfBaua//fbbm5EjR5oFFlgg6hKVbWuH/q8yeLqwlQCJvrBmKEvOP//8YCxQ1vV0neoJ5E2ezPiLPFsE8PnKV75SfUNialAbReKxxx4LQrLFtCOY6cbRuWyZPn164LeRdF2Wo3jJ51EiKHvMmDHm3HPPTbpMYfsZeHz3u99tuXz8JYjklCTylUgiVN/9N9xwg8E5rExBiecFXhfBzItgDFGCUn/00UebNddcc5bdKOkPP/zwLNsaFQmUBiYFkmZeCUM4YMCAWcqp+ku79H9Vc9T1ZydQ9koXNWBio0zlZfZWa0tZBOh38b0hcEgWOfDAAw05zYgG5rvURpE477zzAjvqOKA4aroywYm7RtT2k046yVxzzTVRu7q2YXvJkmb37t27tmX9hwR52AdXJWjGZPx2EeGJcJTMeiYJKxfMJkvah0Dae19Ei6+99lqzySabFFG08zLxGaHPswkrtCgPDPrxA8H+unmFgfOZYCEyHDHw09iA8wIr04TS1sZwXzv1f2Gb9Fk9AVbJmdgKg62UVSPea+RYIny1pH0J0B8TtZLxaVphwpb+l9XnukhtFAmW3J966qlIroBvzEAZeVABGzE9INKRTZg9ZCWiFSWC8plJPOecc2yXCkweCJ1KJ8WAnzwSRDghxjWJqrBJTzOQiLsI+S523XXXuN2pt6f1KVFeidRIa3FgHvMUzJHIG4ONKCY9hFLk5TvXXHMF/QFR0tImPayqn8hzc4gJniVsK0pFVOz7PNemz/It8VY79X957onOKYYA4cYHDRqUqXAmBvmN8J5ddNFFzdxzz23+9a9/BWHXMRck6EEaQTlmxlnSngRQIgYPHmz+/Oc/p2ogk7VMHjUHzEh1csUH1UKR4IasssoqsaiYvWOwUbacccYZwUpD3HXRKIkilTdPRGO5xBtuNjdgaXT33Xc3OCeTbK9bt26Np0T+TyIvzLEoj3jYhNtMK+TnwMTAhTz55JOG8mzCIBKTNgaNkvoTiDK9iWsVL2lMkQhLnPRc8+I++eSTU3XY+Gbw8vddcEKvajBPUIS+fft6hajd+j+v4HZwZVitI2pcGiFZ6v77759oasJK+qGHHppq0u4vf/mLWWSRRdJcXsfUiACJUwlWk1aJIAnyRRddFOT5qlEzu6paC0XiueeeCwbLXbVu+ocwWnEOiE2HOvvKDP8666wTWx7aJXGpybjrShh8syrBQ4fi1Lt375aLfuedd4LEfGmW3piJSTLjylKhNIMlTGHIziupNwFWx9I6z+NPRC6RLA6/JLFjFv+mm26ygsKkjqRTvgsDjLInR1DeRo8e7Z0SEd6rduv/wnbpsxoCrNR/+9vfTnXxrBnisQTALr7ZZ6n5Ylp1byZS/+8477PKRR+eRoiMyXOQ1382zTWKPqYWioTNJIJZ6yeeeKJoTrOVnxQuDnOmuixRMcgjE3hSHH/i8rv006AjxzQsyq47BF4nc5SwzvqcnQC2yD179px9R9MWTHRYxZt33nmb9iR/JQ8MIaJtM4x08GeeeWZyYR4cUaZTOoMecsfk4e4BqpaqUFX/11KldXLLBG655RbDKkMawT9wqaWWSnNo1zG811iFjTPJ5kAmQDGvSlp17SpU/3hNALNtfCLS5ohgHEnerrpLLRQJZsFxao4Sl+Y2UeVHbePFw2pE3AA46+xF1DXK3oYZSVJGacJL7rXXXk6rFhX7vvkCcrpuJlLP70QhSlJW77333txJIaHCyprNj8f1qlrRd2LChAlBtDZX/g/N9UVxw/epOQJU83Ht/r2q/s8XrijgzWYY+CUOHz7clyo6r8cBBxyQyr8KJZvVzjyCTyL93htvvBF7OhMna6yxRux+7agPgSRz98aWkDYAs/R2kFooErZIL8wuZnWWavXG8cO3OUlVFUEqb7vwm8CMwuYkhqkWLxrXM5bMIhOa07YMSCIWbE4l9SZw7LHHBrkL4lrBzAwzNK0Iz1O/fv1iHY8x30mTx6SVOhRxLmGTUbIIG9k84Mt7PZbTGSSRjK6Tpcr+zxfuBOmIEmbtMaVtN2GAT3KvOCG4Q/g7a9Wv6je/+U1guhJ3LUxbyg6HHVcXbc9PIOk+hyUzliI4EP5/7SK1UCQuv/zyIClHFHQyBa688spRuwrbho113NIVsxzY/tdFWIobNmxYotM1ihNRJoqQJHtwnNarSjZYRHs7tUyiK/H7iBNXUcEw0bn11lvjLmNIyOZzcp/Yiv/fjjfffDMw3yJCjM1sIq4czCkIsoBS1eniQ/9X9T348MMPY9+hLpT7qtsXdX0CChCgIUp439BXvfLKK0Fghiy+WlHl8YxtvvnmsSFmUVoYhErqSyBN8Jiwdfjx2fxrw+Pq9FkLRcKWMKbV2YKsN+u1114zJHmKkyocv+PqkrQdJ/Yf/ehHiYMR/FDoWIuMLmFTzmgHikSd4ionse/E/bxQsUm+/fbbI5tPZ7zwwgtH7suykdUrZu7j5Pnnnw9CNsbtr8v2adOmmT333DN2gGJrB8oEZoqsBlaRf8dWt7L2+dT/ldXmqOu89dZbBv+3KGnXxKC2cPKYUWPq5VKSZqvJXq/ohC6Jl1fWjBkzgoAwaULrMym+xRZblFe5kq5UC0XiuuuuC2bNo5hMmTLFsFRUlpAVNs78AjtHzJ58F0yYiFGfNrwknSCzJkXK9ddfbzBhihMStJAoS1JvAp999lkwG47JRKOJ0cEHHxz7G8/aYgYBEydOjD1t8uTJThIrxl6g4B3ErCd/BgEdXAizzvy2VlppJRfFeV+Gj/1fldBQSOMSNeIsfPbZZ1dZPefXfvHFF4MVgriCizBNZhKFCFFxfpVEeCShpKR+BI466iiDr2eSuFpxT7pOFftroUiwDM/MeZSUaaZA5BlWI+Icp4o0/4lqe5ZtJKMjRTss04R6DcvGTMtmjhIe1+qnbVaMsl1HjGq1vkWc/8ILLxjstRGSrjE73+qyehH1dFUmL1VMjLDRn2OOOVwVG/j72Hxu6qRIMACZNGlS4EMEL2YuGxUwZ9D+tyAipB100EGmT58+Lov1oizf+78qIdkG1kSgGTFiRJXVc37tCy64IAgwEFVwkVECd9555y6/i+ZrX3zxxbPliWo+po7f6b8IFMHYib6eRLm81+aZZ546Nme2OtMX77bbbrNtb97Q7skHa6FIYFN29NFHN9+b4Dt2jGVJki2/T174/IBJ4vTQQw8Z/Eiy2lKzyjNq1KjEpHEu2ePwbRsAEuYXMysfhVlOXlB0mHkEZ9ooZ3fuw+qrrx7MFjNjTC4GIu20s4KRh194js3eOzymzD4jvGaeTxSHLJlR81wj6hwmRHCMr3NIyjr2f1H3ooxt9DtxocpRLE888cQyqlHaNYjc9uqrr0Zer8hZY8YwcXlueNdipuibYGHBakke4V2Iw3qUyQ/+Wfi28k4L32uYLpMlvC7y6aefBlGXUMRtgqUK973OeSJs7WNfLRQJW5QkZnHLukFJcaeJX1+leQA/WOqAPwFZq+OWUZMeCpaz0aDLzgBMhnJSxMcJIRqx6fZN6FB4OcWtVBVRXxQqIn2Q2Vzy/wRIAMXzGydkg7f5T8SdV8V2ornE+ZMUXR/8TGymhkVfP0/5de//8rTZxTm2kMk+r7LnafvTTz9ttt1229hTi5ysOvfcc82YMWMir+1jYjpWP8u250e5gFMdzLxs0UQbb3InmK15p0iQo4EHuHv37kECK5QEbgRhCqMEcx2X2aOjrhFuY/nRZi86derUypbsyKaIA1mSdhy2JeqTnBw4w6644opRuwvfxuqJbVaGeNxjx44tvB5ZL5C0UpW1vCzHY9uO/4jkSwJJ9qqtxIQvm3FcSM6y6lH1xEiWdrZD/5elvS6PJazwfvvtF1lku5lk2BLJFm0+a0swyXuPVQmfBB8sckdVIb47JTNpmMYEtE7vm1bus1eKBOY3DIYbhRnEjz/+ODYuPGHc+vbt23hKYf/jLxDnoFx1fHrMqvJk7CV6CzO4zPRXHQ5y5syZ1pkIzHyYUXJpT+/iYUlSgFxcw1YGL6g0nZqtjHbYhx38euutZ20KIVOTjrEWUOLOtIoEvwt+x1GmcY3VRelk0Jh2soEZN5Jr1kHaof+rirMtKShmTZg3tYsQhjXu+T/99NPNPvvsU1hT77nnHrP//vtHlr/llluacePGRe6raiMJarECqEJYcWelrCxrk6xtZEKZiWWbMJ6iv3Wde8t2zar2eaVIMKDFNCGLlJmQzpYJs0gnrTQ8yOabxYma2X2i22C/55MtdFL2YxxPe/funQZJacfYbIzLqASBCA4//PAyLuX1NU477TRz5ZVXxtaRATeO1r6+nJorzuAiKaM1mVHpA99++22rsyb9A/bfCAoXJlN8t5k/+j4r2MirXfq/xjaV9b/NRKOdVjzxi8AENU4IcY7/WVFiM9GuevwQ1WZb2P2o411v8zXfAn54RLG09Z2wYNJ5s802mwULQWUee+yxwH+VfpgIfDihL7PMMsEfE0I9evQwTCLNP//8s5zr8xevFIm0M3CNQNH6cAxkRpabUaTYBrlVL03aZlrimPDQMuPIw84PA3OyqsW26kPdinSGy9t2HDurMgejzqNHj04VOSJv++pwHjkBGFTbpG7JtWxBJnBMxHkzXMElrK4tz0qjIhEywoyU1SzstpsdIlG6yNFTl+gq7dL/hfemzE8Ss7GyHyW8Xx955BGvJpui6plm2zXXXGPIERElPO/PPPNMMKiL2u9imy2xLu9hFDqfJMmfpOi6+po7ymaiFjKhXyb4SihMkOP7EWZLD7fbPnmfbbfddoGfCs+nz1J7RaIRLsthG264YRDlhllr/pZaailnnQPKSpxDbdUZrfO8SBvZ8T8PLtE78JWoataW/BYnnHBCc9W6vletsHVVpOkflsWrWprmRc9z3qmC6SOJsxgI2IRZeCJg1UnI4TJy5MiugT6OooMGDTIbbLDBbIM726x8lCIRckChuOyyy7pWLHhpYRtNFLW6SLv0f2Xy/uKLLwIzHxITxr3XqA+WAosttliQMI2kafwRspmZVMxMMd0gpGevXr287ods/lNlDOTPOeecWB8/ftcXXXRRmbc/8VpMkDEWSOpXEwvKcQCTnCT39U34zfTr1y9xpTi0nCA9AT6MrbaF5wPlBOf3qsZmtnvhlSKxyiqrJC4X2RoTt4/ZdqIBoFgQPpO/POEzbfWrOspJUmboODZR22FF7HAGK2ULs6AMCuOEWVdmKnwTOhhmlFpJSMiSaZz9blx7+/fvHyQni9vf7tt52TEzn8SdWN+s3NRVWAbnBWLLLn/GGWcECkFUG22KRHg81yBkI9HafHxZhfWM+myX/i+qbXm2cS8ZAOJ3iPkEyiGmEny+9957gT8NprDNK1F5rtV4Dqay+FSweu+b2MK+lpEvCcf18ePHR2JhciCPj2NkYQ438k6CTZL/le2SPGM2RTXqXPoyQl/7JmnyRoSJiTFtgp1LQaEguqVv/bNXioTNdMjlzaAsNF6ctAcMGBAMmJNuTJLpwJAhQwxLxFUJHRQdlUthNmLYsGGlOmHzkiMDqE14OS600EK2Q2q7j46bTpscIIQiJHJOnC0mChczx3UxP3F9Uwi7S3jKCRMmWItmpZIZIj7bWVi9IIxklKRRJKLOq8u2dun/WuH97rvvBuYUOE+7VhCy1ss3+/akQB5lBKywKbtFO3pnvX+uj+d55J2Gj9qf/vQnc99998VegsE3Tuk++W6GlcUfkaS+NiH6Fu9wfEyKEN77mHj7FHTGK0UC+zGyP5YtDDDI4EnEBjIvRgmDOVYk4gSzICKHVCWYd2DW5foFwgwWAxRWKcoSmwkZdWD2Ga2/E4TBMs5ZdMCvvfaaIcwlZkwsxa+11lqdgCCyjZjj4GCOg2SS8PyyKtnuwouaiZEoIbzn8OHDo3a1xbZ26v/y3hAGYHFRBfOWmfc83qlEs/NlkiMpIAY27Di5FiUoebZ31rXXXms22WSToi7vXbnvv/9+YO5DMAnea7znsBSh/2KS11dJSppLvTfddFNDWoIiBRNvTF4xL/RBvFIkAMLSEbPgWU08XMEkCRPRjJpXKJIUCR9MbugsCdk2ceJEVziCclAmmOUqK9keS5q2H+IVV1xhiHIh6UwCmGowY2XLgh6S8XWJPKyfy8+PPvoo9jdap1CueZm0S/+Xt/0u/ETyXjvqPJ+ifiWZzBad2BZndpvFApNFSy65ZBRGbfOIgM28vexqlhmxNKlt3ikSVBjtlCR0ZKB1PShOAsJ+Zi9Z6mxUJpIUCc5D+fFhuYkBBS/Vl19+2eDsQ4I/vreinKFM3HbbbaVEJ0qaWSOpEBF4JJ1HgGeZ2XVCOSZJ1eaGSfUrYn+ceSgrWnGrrUXUo8oy697/5WWHn55PWdt9Ul7vv//+IEhBFFtWTzAjLVIIYsI7OErIleXTfYuqo7YZwyo4qyYuhFUXrDwWX3zxwF/3nXfeCSbG4p6RuGvi57TEEkvE7S5tu5eKRGPrsW0kKc6tt97auLnw/5tfvGkUiaKXR1ttNPb3zOIS5YeOFV+DLEKHS4dXdOK6pORSQ4cONYcddliWquvYNiDAM3vwwQfH+ow0NhH/HpIp+bL021i3Iv9n4oUV1Uapu6N5Y1ta+b8u/V/eNhbh3Jm3LgySWMX2xbQp6nfR2LZXXnml8avT/3F6tzmfY4FBvybxmwBm42uvvXbuSjIZix8r+YHizLcwgcOHBLNAfDEYd9rEl2hf3isSQCQyCz4MthBaDG4xvVl++eWD8HQkaJo6dWrsLIDt5rCPcxs7QaKZ9OzZ03oacYPDuO7WAz3ZiWMzKy8MuJIe2LDK2HniSNe4WhPuc/WZ1OlXHWrXVTvjymFFjpcP9qPkp1h11VW9yPERV98ytmdJjkRHfckllwT9QBl18+0aODKSeZXfNGE5SdJni/bkW/3Lqo+v/V/e9jObycx3kcJgKOldge/WhRdeaJZeeukiq5KpbH4TtqzVzROHmQpPOJiocrwz48TXfAlx9c27neeGQTKz7yiaJP/zwYIjbXtayRdFsAtM27IEicEfkmAq1113nbWKUYnvrCcUsLMWigTtxh4szpmZG4QZQ5Rw8zGHYAmICDg2ZSQ8P04hQBu1OTPXdeaPHzjJenho00jRmZRtTqPUrznZS5o6+3QMigLhGd98883A0ez1118PPpkVwxQtymwHczv+iOfOoJAOCaXZloTMpzbnrQu/X5yE4xJmNZdL0APisRPnXiICaQj41v+lqXPcMUl5eOLOs20/77zzgvwrJHwNJ9cw80ARw4wMR3f6NP4IBOGDqUVze1iJt+VFwZSaCRvXgu+FzZ+PPp1gEO0grPiRuZlQrzhQ816bPn16YGINh+axE0opIeaJ0oi1A+80/vBDIAS1j5LHR6KVJLqElSf/yMUXXxyLw4dnqDaKBDNscTCzOJ3wsGMP+eSTTwYPPQ83CXVY0cDZiR99nOlOnP1xeIf5MeDU5WPYsrCOtk8c3XFiTZpxogxmFrDvK0KSlhAJf4ZTuU8yY8aMwK+H54p47TwLzAZjl84+lCMUBBSF5g61lXawtMnSuE+zf620p/FclnkPOeQQg8lgGsGcieyhJMySiEBWAr70f1nr3Xw8s99MnkUJ77h11lknGKgxSGF2GLPhuHcrZdBn1d1EkNVdVirjJG7yMO74tNttSfAoA2uAgQMHpi2u9OOef/75wDcSpYBBPu80JrJQAvDBZBVs2rRpwTOSZtyQtgFHHHFEYO7FdXwSWy6S5noyBmC1oNUkqEymrb/++tZxA8FpmFisSmqjSDBAGDNmTCSnspxvWXHgZWMTwlGyZFdXoWMgBG9Sp2BbBWq17VzbFmrXt+Q9hKMln0GVwqxhFaGTi2ozgQEwRYhanYm6JqaPOOnXaak8qh3aVi0BH/q/sgnwWyPiU5z4EkQkrn5ptrMCvN5668UeSihpVtpdSpLywiCZicdwlcfltVstCyWT8KJxViCtlp/mfAbirEQXsVKU5vpRx/COJU1BGrnzzjudhc23WeRQl6qjE9ZGkSCbH4OlKGG1Yo899oja5XRb0uwCF6v6hrpoME4+SZ0qJjX33ntvIasvSbaIzFKfcMIJLprachlpEui1fJGUBbRLLPIHH3wwcBhOUmZDLMxeHXvsseFXfYpASwSq7v9aqnyOk5N8K9jPqn2dJcnHsQjzEPolW5AYJj9GjBjhJVb64L322qvyuqFs4UPii7kc/plpche5Nv8mYibKbpwUoQjHXStqe20UCVskHzRnskYWLaNHjw7sr23XwdHslltusR3i/T4G8v369UsMF1tklKrlllsulhORD6peAQgrhzJFOFIfpK4+Oo3skuKtNx7L/0R0O+igg5o367sI5CbgQ/+Xu/I5TiR6ny1ICDOw+GbVXZLybGDyzCy4C7GFmw3L591BMA0fhezMBKzwQXwy/7KNQxtZuXbeTwpAQzh8LHOqktooErZZotNOOy2w7S8aYlpHtnaIwmDjHXLm4XYVVzksM/y0KRKYsDAz4INkiSZUdH2LmFUrus5h+QzeTj/99EyZecuaQAjrqM/OIVB1/1cmafzddtlll9hLujTRiL1ICTuSZpNdhWHFjGqzzTazmgcz8Un/5asQjnbChAleVI+oV1iD+CAoCNttt521KkW8hydNmmQOPPDA2OtiBsz7syqpjSJh08jKgkhEApxekoSHnoe/zpJmRgWnPhz3XIstQy/XYmWI2XcfxGZyV3b9fHRCT8OATNWYAaSJqBaW59MsVVgnfbYPgSr7v7IpJoVG9SG8pAsmRM/Bqdom5Fgi8lRe+eyzzwLfrqS+zPdM1oQrJdKlD1KWD2yatjLhRZQpm9nt1ltvbUjG6FKSkvQW6bOaph21UST4ge++++6RbWJwP378+Mh9rjeS7AmlxiYsjzLL49rxk+gZOEHhnV90FI2kcHm0vyibfBxsiY4QJwzebaH84s4rYjtmbEceeWQRRacuk+cNZZoZC9+iXMQ14pNPPgmSG+KozhJ/Fhk3bpw1AkuWsnRsfQh0Sv9X9h0h9Cl+Z3Hi00Auro5pticlhqOM/v37t2TSM3bs2CBcp60+mOVinuuz4IOIBUaVQtAazFaZIHM9lmqlXWnGgFdccYU17G+W67///vuJkZ+qfifWRpGwRZZg8DRlypQs9yb3sWlt4vkRbrTRRrmv03wiD0q4dEV7WTpliY3PIqI+XH755YmOYAyi8QlxLdjk2iIQkUSvT58+ri+bqzxyP/Tt2zfXuauttprp3bt3ELp17rnnDhIvMsDmj5jsfDLzwYw9OSf4RMiKSUz3Xr16Bc8Y96DIBIG5GhdxEit6RD3DeRPTkaxhcHnuSc6z5pprRpSuTe1MoJP6v7LvI5NwtoFt1Y6cLnngC4JPiE3yrsCkeWcSWp58VgsssICtCpXvIyt5HnMi+miiLPFeIzw8E57hu4zZfN5pWBzwXgtzKfE/eX8wZ+a9RrhU8ksUZTbdKlxWk/BJsAkccMpuZXUrLJ/8Xpdeemn4NfITJZmw81VJbRQJEuDYHiyW4crw7OfHkBTTl5vpMnU5g0h+WHGCmQ82rtjmuVipSIriEdYD5Y0fjGu54447zKGHHhpbbJFO3rEXtew46aSTgoR+cYcw8Gc5lJC2PMMoAOR98GmWJa7uLrYTAQRfkqSVvKRrsUqFKd18880XdJp0nPyRq4M/YuR37949qRjtrxmBTuv/yr49SX5e5E9ipr0dJI3vCyu8vIPi8kk1c2BMQHhOZqGThORzvKd9F9qUFHCF7NS811AcWD3gvVZUbinfeKXxIVljjTXMjTfe2FJy1CSzQ7hsuummwfu1Ska1USSAZMssnXcWIQ/8pI43LJMkPwMGDAi/5v5Mm6eAASsKxRZbbBEMWPPMUqcNvVnky+Wyyy4LwujGASNLZp62xZXX6naUXBSfRhMdXhYsyW6zzTbOIoG0Ws+yz8cMj8SBmKKVJSj5KBysWNGRs9IjqTeBTuv/yr5bSZFoGCSmCXlZdr3zXI/Zb1uOorBM2sygnwkKm1Ae/l1p+BCimmPrIpgYEwK2MZcP9v9YC7AKX4QlRF3YkIyPAXySMAbANDDPeCUpCEJ4bR8sNGqlSAwePNiQwS9Khg4dag477LCoXc63sVRHBuzGH1jURVwtb+V16MXkhcEUJjR0noSai5ux5YfBC+Waa66Jasps24oya+JCp5xySqyGTf4KomL5JgyaMb97++23Tc+ePTtmZsZ2H5IcxGznutqHUrHJJpsEJoC2FU1X11M57gl0Wv/nnqC9RCbh+K3aBBPOdhGiPF555ZWJzWFSglUGVkCbhckjzJcvvPDCVCaalMXxeQaUzdcu8zumSCTWo72MIeqeT8Qlu7T9En43HJt2UovVIH6ToSm7rc7HHHNM5T6a1K9WioTtxhXhKW+7gbYoUo3n4SeBU3IrJkc4ZzEr50pYsmX5lr+55porGAAzCE4r5E0YPnx42sMzH2eL990OuRIyA6nhCU8//XRg3udT1Vmx22GHHYKZJPwsePYl/hPotP6v7DuCrxIKN7PrUcI7rGrH26h65d2WlHG6sVzelQSxwLQYUx4YYR7FSmscr8bz+Z/VDc5ZZJFFmnfpe40JEKELx+s0q1E8R5hD8f5ZeOGFI1s9c+ZMc/vttwf+EGnGY6y6M7b0QTmtlSJhC8nHoJiEMmUJM9CYEaVJl84Muy0GcFKdcYQrKypVUl3oTHHEKmpZk5caJmxxQnZzmyN23HnaXi4BnxIaRbWc1cJBgwaZPfbYI4iCFnWMtvlBoJP6v6qIT506NTDFbB4c43CLs3W7Kd1pbNxd3AtW0Hl3L7rooi6KUxmeESCiEspBmoF/WHUmtDB9RrlgtQtrEMrJkreD95dPGb9rpUjQydnsG5999tnIZcjwBrr+TBMiNbwmKxh5TSuSwvOF1yj6kx8AuSOKzHJKRIshQ4bENgXTNsLfSvwmkNZ8wIdWYOvKwMJllDUf2tUudeik/q/Ke8Zghog0KBVEFcK/q4wAJlW0+fXXXw9CSDcrTi7rwvsSZ9t2ZeiSVZ3LwmcTU/cy5eqrr84dLbKIetZKkQCALTslS0wsI5YpOE/deuutiZdkJh/zJMKcZZXPP/88GOi0GvUm63Ubj6dTvP76602PHj0aNzv/n+gX+GpESdmrTlF10LZ0BJIc5tOVUu5RZ599drBCUe5VdbUkAp3U/yWx0H53BJKyBbdyJXwiiHRV5KRbK/XTuW4JoHyzekfEyyKFlQiUiCISAbdS79opEnio41gdJcSY33jjjaN2FbaNWMjkckgzs9FKPG7s8QhJhzLx3HPPGWZU0lzTRcPpFMnUGGfb5+IalIG5GCHnsGGNEpYQx4wZE7VL2zwjgHMev8UsuSJQVpkoIPY2M6KYU+BbhPMZTn8ff/yxeffdd7vij9Np44vh8nfQTk6lnj0SLVWnE/q/lgDp5FwEkiJW5SkUM+Zhw4Z5Ybuep/46Jx8B3lGMT5Kyp+cr/UtfG4IEEDreN6mdImHL8leV2Qsz9ccff3yqe4u9JAPzVoVZOhLrMKtC2NEiNGGiPbG8TXbJPCspWdv40EMPmT333DP2tHbJsBrbwDbbYQuO0NzUgQMHBrHYoyKkNB/b/J3BP3lkSHaH3WgW5aW5LN9CCzfXT9+/JNCO/Z/ubTUEXJnOsWKObxiBXySdSwCTdyxVkqJ6ZiG01VZbmfPPP7+QvF1Z6hF3bO0UCRqCnf7RRx89S5vQBJmxrkJ4qe26666pHK9xsMGpxvXs/ocffhjM5DOomjZtWvDH/yRzIqNw0qwtS2bMBJNVEptxZpPLXpbde++9zQMPPBB7C4mr3CkJb2Ih1GgHYZIJNGCL+MIqBCHsUCRcCKtaL730UqBYkMwniwMb19eKhIu7UH4Z7dD/lU9NVwwJMPg75JBDzBtvvBFuyvRJZDHO5z0qEQFW0ZnYxkomTVSnOGJEZuK5Im9HK5E/48p3tb2WigSNnzx5clfOA0JwkVWxSskSUg6nTuzcygzbxYPNag5/06dPDzo8VhmYRSEsXRkrDrb7kxQulJwY5K6Q1I/AM888EzgdEgwBJZdnjsADKP4orEVm+P7ggw+CFTsUiiSlwlUCyfrdofavse/9X/vfAf9byGTbWWed1TWuSKoxkyBMfu24445yqE6C1cH7MUMnuSEhgNOuUmy77bZBaFnygNVBaqtI+Aj30ksvDTqiNHXjQcH0o0xlIk29qjgGe3o6ZFso3REjRph99923iurpmm1CgOdsypQpwSod5k8kCGJlEMWGlUJW5CQiIAKdTYBJwXHjxgXBURpX8lEcmADp3bu3IdcRDq/dunXrbFhqfSYCH330UaBMsPLNyjlKBmNAni2iUS6zzDKBD0Tdwi1Lkcj0GNgPZtaLhGm2AXFjCZgQ4Ziz4IILNm7uqP8xSSD0ZlzG8hAGOUIY8ElEQAREQAREoAwCM2bMMAz+MPPt3r17GZfUNUSgdgSkSDi+ZWiZRHFKK4Srve222wpL8Ja2HlUch/8GUXowfbEJqzcXXXSR7RDtEwEREAEREAEREAERKJmAFIkCgONgQ/i3tDJ48GBzxhlnpD28LY7DcZpoUGki7BD2lghSEhEQAREQAREQAREQAX8ISJEo6F4QxYEEdGmFSFS+JRlJW/csxxHhimRlI0eOTHVa//79zSWXXJLqWB0kAiIgAiIgAiIgAiJQHgEpEgWxnjlzpmEQnNZLf9111w08+wuqjhfFvvfee+a4444zd911V6r6EEqPEJ4K+ZoKlw4SAREQAREQAREQgVIJSJEoEDdJ4kjollZw0i47d0PaurV6HCz222+/THG6yWJOHGWJCIiACIiACIiACIiAfwSkSBR8T5hR32effVJd5f777zfLLbdcqmPrdBC5A8j22RhKL6n+hNLNooQllaf9IiACIiACIiACIiACbglIkXDLM7I0ZtaHDh0aua9x46RJk4IY1Y3b6v4/mYbJWkzm0LQyatQos+eee6Y9XMeJgAiIgAiIgAiIgAhUQECKREnQGUjvtdde1ln5p556yiy00EIl1aicy5AEjCQ+aYXoVUSxkoiACIiACIiACIiACPhNQIpEiffn8ccfNzvttFPkFXEsJutuuwlRmgjdmmTWRPuJztS3b992Q6D2iIAIiIAIiIAIiEBbEpAiUfJtvfvuu4MkbM2XJcv1VVdd1by5Lb4TqWn8+PGxbVl55ZXN2LFjzQorrBB7jHaIgAiIgAiIgAiIgAj4RUCKRAX3g/CnQ4YMmeXK99xzj+nVq9cs29rly+TJk83uu+8+26rEsssuG/iODBgwwMwxxxzt0ly1QwREQAREQAREQAQ6goAUiYpu84wZM8zEiRODqzOQXmSRRSqqSTmXffHFFw0ZqnG+7tGjh1lppZUC34k555yznAroKiIgAiIgAiIgAiIgAk4JSJFwilOFiYAIiIAIiIAIiIAIiEBnEJAi0Rn3Wa0UAREQAREQAREQAREQAacEpEg4xanCREAEREAEREAEREAERKAzCEiR6Iz7rFaKgAiIgAiIgAiIgAiIgFMCUiSc4lRhIiACIiACIiACIiACItAZBKRIdMZ9VitFQAREQAREQAREQAREwCkBKRJOcaowERABERABERABERABEegMAlIkOuM+q5UiIAIiIAIiIAIiIAIi4JSAFAmnOFWYCIiACIiACIiACIiACHQGASkSnXGf1UoREAEREAEREAEREAERcEpAioRTnCpMBERABERABERABERABDqDgBSJzrjPaqUIiIAIiIAIiIAIiIAIOCUgRcIpThUmAiIgAiIgAiIgAiIgAp1BQIpEZ9xntVIEREAEREAEREAEREAEnBKQIuEUpwoTAREQAREQAREQAREQgc4gIEWiM+6zWikCIiACIiACIiACIiACTglIkXCKU4WJgAiIgAiIgAiIgAiIQGcQkCLRGfdZrRQBERABERABERABERABpwSkSDjFqcJEQAREQAREQAREQAREoDMISJHojPusVoqACIiACIiACIiACIiAUwJSJJziVGEiIAIiIAIiIAIiIAIi0BkEpEh0xn1WK0VABERABERABERABETAKQEpEk5xqjAREAEREAEREAEREAER6AwCUiQ64z6rlSIgAiIgAiIgAiIgAiLglIAUCac4VZgIiIAIiIAIiIAIiIAIdAYBKRKdcZ/VShEQAREQAREQAREQARFwSkCKhFOcKkwEREAEREAEREAEREAEOoOAFInOuM9qpQiIgAiIgAiIgAiIgAg4JSBFwilOFSYCIiACIiACIiACIiACnUFAikRn3Ge1UgREQAREQAREQAREQAScEpAi4RSnChMBERABERABERABERCBziAgRaIz7rNaKQIiIAIiIAIiIAIiIAJOCUiRcIpThYmACIiACIiACIiACIhAZxD4H5hPEPOBnKJRAAAAAElFTkSuQmCC" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With an input of 1 or 3 the output will be :\n", + "\n", + "![example_polynomial_function.png](attachment:example_polynomial_function.png)" + ] + }, + { + "attachments": { + "m_function.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like easy for now, but let's imagine another function called m :\n", + "\n", + "![m_function.png](attachment:m_function.png)" + ] + }, + { + "attachments": { + "example_m-function.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Strange we don't know the function inside, other wise we know that for an input of 1 and 3 the output will also be (like other example):\n", + "\n", + "![example_m-function.png](attachment:example_m-function.png)" + ] + }, + { + "attachments": { + "m_machine_learning.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can say that we have a dataset of input and output (all the example above) but we don't now the formula to transform `x`into `y` and that's were *machine learning come* !\n", + "\n", + "So machine learning is the process to find an algorithm (here a simple function) that fit with all the data we give it in parameter\n", + "\n", + "![m_machine_learning.png](attachment:m_machine_learning.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many technics are possible in machine learning like the famous neural networks, the random forest, the k-nearest neighboors, and the most simple technique : the linear regression !" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 2.0 The Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1: Understanding the Formula\n", + "\n", + "\n", + "the formula for linear regression is just the representation of a simple polynomial function like this : \n", + "\n", + "$$ f(x) = a \\cdot x + b $$\n", + "\n", + "But today, we’re not going to stick to the pure math version. Instead, we’ll look at its “machine learning transcription” (same idea but with different names) since we’re going to talk about neurons and all that jazz. Yeah, I know we’re not technically in neural networks yet, but hey, this formula works for a single neuron too! Here’s the breakdown:\n", + "\n", + "A neuron is basically the simplest function in machine learning. With one input and one output, its formula looks like this:\n", + "\n", + "$$ y = x \\cdot w + b $$\n", + "\n", + "Where:\n", + "-\t $ x $ is the input.\n", + "-\t $ w $ is the weight of the neuron.\n", + "-\t $ y $ is the output.\n", + "- $ b $ is the bias of the neuron, which is just a value we add to the result.\n", + "\n", + "If we had two inputs, the formula would expand to:\n", + "\n", + "$$ y = x_1 \\cdot w_1 + x_2 \\cdot w_2 + b $$\n", + "\n", + "The goal here is to modifie $ w $ and $b$ so that our predicted output $( y_{\\text{pred}} )$ matches the actual output $( y )$.\n", + "\n", + "Alright, let’s get started by creating a dataset for our neuron to learn from!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Task: Create the training set\n", + "train_set = [\n", + " [0, 0],\n", + " [1, 2],\n", + " [2, 4],\n", + " [3, 6],\n", + " [4, 8],\n", + " [5, 10]\n", + "]\n", + "print(\"Training Set:\", train_set)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Task: Visualize the training set\n", + "\n", + "x = [i[0] for i in train_set]\n", + "y = [i[1] for i in train_set]\n", + "\n", + "plt.plot(x, y, 'ro')\n", + "plt.axis([0, 6, 0, 12])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Initialize the Weight and Bias\n", + "\n", + "We need to initialize both `w` and `b` with random values beetwen 0 and 10 at the start : " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "random.seed(0)\n", + "\n", + "\n", + "mini = 0\n", + "maxi = 10\n", + "\n", + "#TODO: randomise the weight and bias beetwen 0 and 10\n", + "w = ...\n", + "b = ...\n", + "\n", + "print(\"Initial Weight:\", w)\n", + "print(\"Initial Bias:\", b)\n", + "\n", + "assert w > 8.44 and w < 8.45, \"Weight is not correct\"\n", + "assert b > 7.57 and b < 7.58, \"Bias is not correct\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### step 3 : Make prediction using the neuron formula\n", + "\n", + "With the formula of above, calculate the `y_pred` of each input `x` in the train_set in a function call **forward** \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : Define the forward function -> neuron function\n", + "def forward(x):\n", + " ...\n", + "\n", + "#TODO: Test the forward function\n", + "for x, y in train_set:\n", + " y_pred = ...\n", + " print(\"Prediction:\", y_pred, \"Actual:\", y)\n", + "\n", + "assert forward(0) > 7.57 and forward(0) < 7.58, \"Test Failed\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# graph of the prediction and the actual data\n", + "x = [i[0] for i in train_set]\n", + "y = [i[1] for i in train_set]\n", + "y_pred = [forward(i) for i in x]\n", + "\n", + "plt.plot(x, y, 'ro')\n", + "plt.plot(x, y_pred, 'bo')\n", + "plt.axis([0, 6, 0, 50])\n", + "plt.show()" + ] + }, + { + "attachments": { + "mse_loss_explication.png": { + "image/png": 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mOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgq4vDLhAAAAHGlET1QAAAACAAAAAAAAAlgAAAAoAAACWAAAAlgAAOT8lBPxDAAAQABJREFUeAHs3Qd4FNUWwPFDgEAooZfQOwIiIFWUqoKAFUQBQVTsitgVRFRAmojtqSh2qvAQQekgTekIiAiCSCf0EjoJ4c0Z3i47u7Ob3SSb3ST/+b64M3dm7tz5zRKzZ8+9N8slYxEWBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgEwtkIVCaiZ8+t44AAggggAACCCCAAAIIIIAAAggggAACpgCBUt4ICCCAAAIIIIAAAggggAACCCCAAAIIIJDpBQiUZvq3AAAIIIAAAggggAACCCCAAAIIIIAAAgggQKCU9wACCCCAAAIIIIAAAggggAACCCCAAAIIZHoBAqWZ/i0AAAIIIIAAAggggAACCCCAAAIIIIAAAggQKOU9gAACCCCAAAIIIIAAAggggAACCCCAAAKZXoBAaaZ/CwCAAAIIIIAAAggggAACCCCAAAIIIIAAAgRKeQ8ggAACCCCAAAIIIIAAAggggAACCCCAQKYXIFCa6d8CACCAAAIIIIAAAggggAACCCCAAAIIIIAAgVLeAwgggAACCCCAAAIIIIAAAggggAACCCCQ6QUIlGb6twAACCCAAAIIIIAAAggggAACCCCAAAIIIECglPcAAggggAACCCCAAAIIIIAAAggggAACCGR6AQKlmf4tAAACCCCAAAIIIIAAAggggAACCCCAAAIIECjlPYAAAggggAACCCCAAAIIIIAAAggggAACmV6AQGmmfwsAgAACCCCAAAIIIIAAAggggAACCCCAAAIESnkPIIAAAggggAACCCCAAAIIIIAAAggggECmFyBQmunfAgAggAACCCCAAAIIIIAAAggggAACCCCAAIFS3gMIIIAAAggggAACCCCAAAIIIIAAAgggkOkFCJRm+rcAAAgggAACCCCAAAIIIIAAAggggAACCCBAoJT3AAIIIIAAAggggAACCCCAAAIIIIAAAghkegECpZn+LQAAAggggAACCCCAAAIIIIAAAggggAACCBAo5T2AAAIIIIAAAggggAACCCCAAAIIIIAAAplegEBppn8LAIAAAggggAACCCCAAAIIIIAAAggggAACBEp5DyCAAAIIIIAAAggggAACCGQYgU2bNsnu3bvl2LFjcu7cOSlYsKA0atRIihQpkmHukRtBAAEEEAiOAIHS4LhSKwIIIIAAAggggAACCCCAQBoJxMfHy3fffSfffPON7Nq1y/aq999/v7z22muSM2dO2/0UIoAAAgggQKCU9wACCCCAAAIIhKXA+tgNznbViqnpXGcFAQQQQAABV4E9e/bI008/LWvXrnUttl1v3bq1fP7557b7KEQAAQQQQIBAKe8BBBBAAAEEEAgrgTFrxsmUdROT1aYqMdX9Oq9Gsav9Oq5miaQDtARx/aLkIAQQQCAoAtq1vnnz5hIbG+usP3fu3NKuXTspV66cHDx40Mwyde40VkaPHi1NmzZ1LWIdAQQQQAABU4BAKW8EBBBAAAEEEAgrgQ5f3hlW7UlOY+6qfY90rdslOadyDgIIIIBAAAJz5syRRx55xHmGjkWqGaP58uVzlumYpT179pStW7eaZV27dpW3337buZ8VBBBAAAEEHAIESh0SvCKAAAIIIIBAyAW0u33/Ga9fbkcW4+WS8eN4DXnrAmsAwdLAvDgaAQQQSI6ABkFvueUW56krV66UYsWKObcdK3PnzpWHH37Y3GzQoIFMmjTJsYtXBBBAAAEEnAIESp0UrCCAAAIIIIBAqAVS0u0+1G23u74OBTC47SC7XZQhgAACCKSSwHvvvSejRo2S66+/3ny1q1bHL73zzss9FmrVqiXTpk2zO4wyBBBAAIFMLkCgNJO/Abh9BBBAAAEEwkkgI3S7d/ec3ONH9yK2EUAAAQTSWGDMmDHmjPd62caNG8v48ePTuAVcDgEEEEAgPQgQKE0PT4k2IoAAAgggkEkEMlqglO73meSNy20igEDYC3Ts2FG0W74uOqZp3759w77NNBABBBBAIO0FCJSmvTlXRAABBBBAAAEvAoF0vS+Yt7BUM2avL5qnqJfaLhdvPPCnz/2B7twS+5dfp2i3+051OkutmJp+Hc9BCCCAAALBEZg9e7Y8+uijzsp/+OEHqVu3rnObFQQQQAABBBwCBEodErwigAACCCCAQFgIBBIs1QaTtRkWj41GIIAAAmEpoGOT3nfffXL69GmzfZpZOnz48LBsK41CAAEEEAi9AIHS0D8DWoAAAggggAACbgLrYzfIhLXjxd/sTYKlboBsIoAAAgjIvHnzpEePHk6JChUqyNSpUyU6OtpZxgoCCCCAAAKuAgRKXTVYRwABBBBAAIGwEiC7NKweB41BAAEE0o3A559/Lm+//bazvTExMaJd7kuUKOEsYwUBBBBAAAF3AQKl7iJsI4AAAggggEBYCRAsDavHQWMQQACBsBa4cOGCObv9xIkTne3UTFKd5b548eLOMlYQQAABBBCwEyBQaqdCGQIIIIAAAgiEnUCgAdN+bQcwkVLYPUUahAACCARP4MiRI+akTatXr3ZepFq1ajJ27FgpVKiQs4wVBBBAAAEEvAkQKPUmQzkCCCCAAAIIhJ1AoMFSxi4Nu0dIgxBAIJMLXLp0SeLj4yUyMjLVJHbt2iXTpk2Tr776SjRY6ljq1atnluXLl89RxCsCCCCAAAI+BQiU+uRhJwIIIIAAAgiEo0AgAVOCpeH4BGkTAghkRoH58+fL4MGDZevWrTJ06FC55557JCIiItkU27dvlzfffFMWLlxoW0edOnXM7vYFChQQ7X7fsGFDqV69umTLls32eAoRQAABBBAgUMp7AAEEEEAAgQwkkHjhpJxc/4Uknjsuuat3lshCV2Wgu7PeSiDBUj2TgKnVjy0EEEDAl8Dfe0/Jz2tipW6F/NL4qkISmS35AU29zpo1a6R9+/aWSzZq1MgMmJYrV85S7s+GZqbWrVvXkkHqz3m5c+eW1q1byy233CI33ngjQVN/0DgGAQQQyEQCBEoz0cPmVhFAAAEEMrZA4vk4if3mFkk8H3/5RrOIFL59iESVvTHD3vj62A0yYe142RL7l1/3mN6DpWfOnJH9+/dLsWLFRD/ssyCAAALBEDh17qK06rNILiZeMqvPmSOrfPtiAylXJFeyLzdv3jzp0aOH7fk6G70GPQNZTpw4Iddcc43tKfr78fTp07b7XAt13NLRo0dLjRo1XItZRwABBBDIxAIESjPxw+fWEQiFgI4bNWLECHNsqltvvVWaNm0aimZwTQQypMDpzZPk6JxhlnvLUfYqKXrHaEtZRtwINLs0PU30lJiY6Bx7b/369ebjK1OmjOiMzjExMRnxcXJPCCAQYoHRi3bJf6ZstbTi1utLyusdk99LYfHixdKtWzdLnY4NDWxOnz5dypcv7yjy63XgwIEyatQo81it4+WXX5YuXbo4xz89f/68HD58WDZu3CgrVqyQX375Rf79919L3XrOU089ZSljAwEEEEAg8woQKM28z547RyAkAl27dpUlS5Y4r92qVSsZNGiQFClSxFnma0UzDvSPXQ0OVKpUSW644Qa6TPkCY1+mEjix6j2JWzbOcs+RRUtIsU5TLWUZdSPQYGl6yC49ePCgvPjii7Jo0SKPx/b000/LSy+95FFOAQIIIJBSgd6jN8ova/ZbqilaMKf81O96S1kgG0ePHhUdM9TbovsmT54sWbNm9XaIbfmmTZskNjZWatWq5dfM9jo+6tKlS+XQoUOikzzdddddUrhwYdu6KUQAAQQQyHwCBEoz3zPnjhEImcCxY8ekdu3aHtfX8anGjh2bZMDzjz/+kNtuu81yvp77ySef+PWHseVENhDIgAKx390kCcdPWO4sW/58EnP/PEtZRt8IJGAazsFS/VJIs69cZ3B2fXZPPPGEvPrqq65FrCOAAAKpItB+0DLZe/CMpa6sEVlk6YiWlrJAN7744gsZMGCA19N03/333+91PzsQQAABBBAItgCB0mALUz8CCDgFZs+eLY8++qhz23VFP+zrh35fS8+ePc3up+7HVK5c2SzPlSv542a518k2AnYCF45slqOznpOLZ05KjtI1pfAtn9odFpKyhOP/Sux393pcO3uR4lK8808e5Rm9IJBgqVqEW8B02bJl0qlTJ5+PSb8kateunc9j2IkAAggkR6Dx8784xyd1PX/u4GYSHZX8GeN1KJEPPvhA3n//fddqnevafV67yOfNm9dZxgoCCCCAAAJpKUCgNC21uRYCmVzgvffe8/qHsdJoYKBEiRJelXR2Uu1eZbcMGTJEOnfubLeLMgRSReDsjnlyeFpvS13F7hsdNrPKH13wqpzeMN/SPt3I1/Rhia79mEd5ZikIJGAaLsHShQsXSvfu3X0+ogYNGsj48eOTzMT3WQk7EUAAARuB/cfPyR1v/mazR2T6gBukcN4ctvsCKdS/2z791P7LRsYMDUSSYxFAAAEEUluAQGlqi1IfAgh4FXj88cdl5syZXvdrxqiOxWe3aAaCrwH+77jjDvnwww/tTqUMgZQJXLooJ1a9L3ErJohcnvzXWV/Rez6VHMXrObdDtpIYL3tG3iCXEhKdTchidJHM3/wpyXO174Cb84QMvBJIsLRKTHXpVKez1IqpGRIR7W7ftm1br9fWSZwGDx4sjRs3loiICK/HsSNzCWzac1ImLt0jiRdFihfMId2alZU8OQMb59FV7PjpC/LZnB2SPVsWqRKTR1rXKS7Zs2ZxPYT1DCywdPMReW7kOts7nDmwiRTME2m7L5DCCxcuyO233277BThZpYFIciwCCCCAQGoLEChNbVHqQwABrwJNmjSRXbt2ed1frVo1mTVrlu3+AwcOiGZQeVu0+/28eZlrHEZvFpSnlsAlOblulMQt/1oSLyTYVhougdK4tZ/IiSVfW9qYt34HyX8d41c6UNbHbpAJa8fLlti/HEU+X0ORXaoTN2nmvLcxSfV36MiRIyVPnjw+287OzCUQf/GStHhlocS7fFHS5aay0uvWSsmCOHzyvHQYuEzOnTeirv9fYopEycRXGklkNoLzDpOM/Prp7H/lm5nbbW9xzqCmki9Xdtt9gRZu2LBBbr31VtvTdKI6nbCOBQEEEEAAgbQWIFCa1uJcD4FMKnD+/HmpUqVKkne/evVqKVKkiMdxK1eulI4dO3qUuxZs27aNbqiuIKwnT8DIII0zAqQnV3zrNUDqqDhcAqV7P2ssiefjHc0SzSYt+fhiyZItp7OMlcsCgWSXpnWw9Mknn5Tp06fbPioNko4aNUqioqJs9we78Mz27XJkjvFFlpHdn71IUSnStp1kTcG40AknT8qByZMkS/bsElWmrBRobMykHeBM18G+5/RSvwY2273+q6W52Y2A5rxBzSRnZOCBzZ6j1snKjUcs9enGM+2ryH1NS3uUU5DxBB74YLVs2m6dGNBxl/OHNE9RtrKjHserZsjrF0Dui2aVrlu3TiIjU5696l432wgggAACCPgSIFDqS4d9CCCQagL//vuvtGjRIsn6PvroI7MrlvuBY8aMkddee8292LK9efPmkAURLA1hI90KnNk6VY7NH5JkgNRxg+EQKD21cYzR5g8cTTJfc19zsxRsPshSxsYVgUCCpXpWv7YDgt4Vf/78+fLQQw9daaTLWqiDpHLxomy4+3ZJiDvubFWxh56QEvd1dW4HspJw/LhseqS7JBw97Dwt6qqrpcp7H0kEQRGnSSArdhPvPN+xqtx7falAqhFfXa57dagiXZoQKA0INJ0e3OLVRXLmnH1PikXDWiQrAO+N4syZM9KyZUuJjY31OIQJ6zxIKEAAAQQQSAMBAqVpgMwlEEBAxJ/JSdRJs0aHDx/uQda1a1dZsmSJR7lrgY7tR5dUVxHWAxE49ee3cuyX/wRyioQ8UJqYIHs/b2IJ7GbJGiElHl0oEdlDk3kYEGCIDw4kYBrM7NJz586ZY47adbmvV6+ejB49WnKlIHszpcwJcSdkw13WcVOzReeXGt9PSVZgc/vgt+X4vBkezSr10mtS5BbrdTwOosBWoOuIVbJ1V5xlX7M6RWVYd//H2r1gdN1v9+avEnfqSna6a4UfP32t1KtUwLWI9QwocMkYi7vRc/O93tny926ULKk8XK0OndSjRw+Pa+pYzDppHQsCCCCAAAJpKUCgNC21uRYCmVjgq6++krfeeitJgUKFCol2v3edpESDB9dee22S5/7555+SN2/eJI8L5ICLRiaVZjloRux2o+tpXFycFChQQEqVKiUNGzYkgzUAzFOnTolO3pDd6GqrAe0sqf1JK4C2eBxqTIa0+z+NPYodBVGV60pUxdZydJY1SzPUgdITq0ZI3DLrh8johvdKvob2k6I57ofXKwLhECwdO3as9OnT50qj/r9Wp04d+fbbbyVfvnwe+9K6YH27myXx3BnLZUu/2k8K39zaUpbURtz69bLt+SdtDyv1cl8p0rqN7T4KfQsMmvy3TF2yx3JQ8cJRMrWv999rloONjZe+2SCL1x10Lza361QtKP95rLZkM4b1YMnYAjqRV+vX7L+Y1iEdfh2edO+g5Ag98sgjMmfOHI9T9UtyncSOBQEEEEAAgbQSIFCaVtJcB4FMLqCD8k+cONEvBZ3QSSd2cix6np6f1KKTAkRHRyd1mM/9l4xUCq1n7ty58ssvv4gGX70tGtT94YcfpFy5ct4OybDlOh7su+++KwkJ9l3z3G/8pDEe4dKlSy3FFSpUkOrVq0vVqlXNHx3DtnTp0iEbZ3b3R/U9ZrWPyJVTCt86wpjZvr6c3T5HDv9kHf4hlIHSxPizsu/z5nLp4pWZ7iOyZ5OSjxkfcCOyWazZSFogVAHT+Ph40a717t1O9feL/h7S13BYtr7yopxavczSlAK3tZdyz75gKfO1kWjc66Ye98uFvfaT+lX+aJTkMX4nsAQuoDPevzvxb48Tl45oKVn9CG5+OP0fGTt3p8f5WlCrSgH55PE6BEltdTJe4bb9p6XLkOW2N5Y/OlJm929iuy+lhTt27JBmzZp5VPPiiy9Kz549PcopQAABBBBAIFgCBEqDJUu9CCBgEdDxSTUr05/l9ddfl4cffth56J133ilr1651bntbSUmgdM2aNTJz5kwz8GnX/dXbNe+9914ZNmyYt93JKtfg486dO2X//v2iWZhFixY1J8LSiQ3CZencubNH4DM12qYZdJ999pkUK1YsNaoLqI7DMx+Ts1t/N8+JiMoheWp3kHz1nzW2L2dQhVug9OjC3nL6j3mWe8x3fXeJrssswRaUADZCESydPHmyPP/88x6t1O6m2u00XJa9X46Sg+O+sTQnqmoNueqTzy1lvjZ2jBgux6ZPsT0kuvnNUrHP60zoZKuTdOHaf4/L4x+u8ThwQp/rpHzRXB7lrgVTVuyTIeM3uRY512tVNoKkTxAkdYJkgpWVW49Jz48v/7/Q/XYrl4mWMc8bXyoGaXn55Zfl+++/t9SuX5zrF+gsCCCAAAIIpJUAgdK0kuY6CGRiAQ34aTd1fxfNrtLJm3TZtGmT3HLLLX6d+scffwTcRVXb1q9fP5k9e7Zf13A/SCcg+Prrr92Lk7V9/vx5cywuDbyePn3aow7teqZd0zRIqd3XQ7noUAiBBJQDaWu7du1EJ3AIxZJwfLtcumTM6l2gosflz/zzkxyZ0d9SHqqM0oun98u+r26zZMCSTWp5NMneWB+7QSasHS9bYv9Kso4qMdVlcFvrcAxJnuR2gP5+099zrsszzzwjL7zgf6am67nBWj88d47sHvKWR/V15moGc9Izq8eOGyv7v7T/dx3d7Cap+Fo/gqQeuv4XnDybIDf1XuRxwqCHasqN1xT1KHcUzFl3QF7/xr7nhAZJPzYySbNnvfxlkeMcXjO2wK+bjsgLn62zvckbaheRdx+4xnZfahR6G89ee6SULFkyNS5BHQgggAACCCQpQKA0SSIOQACBlApMmTJFnn1WM/Osy9ChQ+WVV16xFv5/SwMHOnnJ4MGDZeTIkbbHuBf+/vvvfndT1bFHJ0yYYDsuoHu9vra7dOlittHXMUnt0wzSadOmmZmp7t1v7c7VgOmHH34omn0ZqiWQDOHktDGQZ5mc+pNzjt1kTzH3T5Rs+csnp7oUnXNwajc5v3OzpY58Nzwg0dc+ZSkL1Ub8sX/kfOwqiSxytfFTM1TNSNF10yK7VIf20C8GXBftar9y5cqQDUHh2hbX9VN//y1bn3zItchcrzZ6ouQs4TuAcWTBL7JroJEtarNEN71RKvZ9gyCpjU2gRc1eWSjnzl+0nHZPyzLywu2VLWWOja9/2SEjp21zbFper66YX0Y+eS1BUotK5tj4ZcNB6f3lBtub9fV+sj0hwEL9wliH4XFfBg4cKN26dXMvZhsBBBBAAIGgCBAoDQorlSKAgKuAZkb997//dS0y1zdv3ix33XWXRzaV7tRZnjULVYOBdtmVHpUZBRpc8KfLtgYmtavr1KlT7aoJqOynn36Sa65JfnaFdq3v3r27OYFVIBfWbvg6VEDZsmUDOS3VjrXrHpdqlRsV/fPPPyHPmnW/n7jVH8qJpaMtxaWfNsZdjUjb7N4LhzbIgfHWgJWZTfr4r8YoAVkt7QvGxoWD6yXx/AnJEdNAsmTLabnEub2/ybG5r0tC3ElneYGbnjfGnezs3E5PK5pd2n+GfYDP/T7uqn2PdK3bxb3Y53b//v3lyy+/tBzz+OOPS+/evS1l4bCRYGS5b7i9lUdTKgx5zxiiooFHuaPg2PJlsuM1+8nFCJI6lFLn9ZH//C5//HPMUpndhE6Jl0QGTtos05futRzr2CBI6pC4/PrnrjhZYwxtcHXpvFK3YgHrzgy4NfP3/fLmdxtt76x3l2pyZ4MStvtSq1D/JtLMUtdFxy797rvvXItYRwABBBBAIGgCBEqDRkvFCCCgAufOnTNnrHcPdurYezoGn45HOWjQIA+sxx57TK6++uqABvDXP6zLl086uy+QLFUdG0vboRMPaRBWZ5+OioqSnDlzSoECBcxyj8b7WaAm999/f8BBUkf12iad7CVbtrSfuGfPnj3ywAMPyNatWx3NSbXX9u3by3vvvZdq9aVWRcd/Gygn11wJrmcxJkgp9fTK1Kre73r2j28n8YcOWo7P1+Qhia7zhKUsGBvHlw+Vkysvf+kRkT2rxDw0WyJy5DMvdW7XQjk09SXLcADmDqPXbvH7xkr2glWC0aQ0qdPf7NJAuuJr5pTdF0EzZsyQGjVqpMl9BXqRDR3vkISjhy2nFX3gUSnZrbulzLFxcNqPsveDdxyblte8jZtJpTcHkElqUUnZhrcM0Q+erCONqhQ0K9cZzZ8zsgX/MgJ/dkv1Cvnl86fIJHXYDJr8t0xdssexKZNebyxlCkU5t3Vl675T8vOaWFn/7wk5dvKCROXIJjXKRUvNstHSoHJBKVHA+oWS5WSXjTNGNvBPq2NFJ1M6dOKcREVmk2pGcLZG6WipUz6/ZDF+l6bF8sPyfTJ0gnU4EMd1f+5/gxSJzuHYDMrrp59+KkOGDPGoW//eiIyM9CinAAEEEEAAgdQWIFCa2qLUhwACFgEd+/PRRx+1lOmGdrl/8sknZffu3XLDDTd47HfM9Gw3DqYGCO0mhtIZ6OvWretRl2vBsWPHpHbt2q5FHuuarfnEE0+IBuyCNSbWmTNn5MEHH5Tly5d7XN9RoO3QwK92z/W2aFZsUvfj7dyUll+4cEF0Nnt/Fw0MaRaxTrqlXetXrVrlkS3coEED+fjjj80JrPytN62OOzr/JTm9caHzchGR2aXk40ud22mxcnbnAjk89WXLpSKMD9MlH0ubbFL3IG3Ru/8jOUo0lIRTsbL/2zvl0sVES9scG9kLFTaCpTMdm+nyNbWDpb/99pvo0B2ui/6b/+uvv1yLwmp92xt9Je7XBZY22U7olJgou7/4XA5/b83AdpxIkNQhkbqvW4yAXbdhKzwqzZkjq7xyz1Wy/eBpc2b7i5pSarNkhCDpnqNnZdTsHbJu2zE5cvy85M2TXSqVzCPt6hWX5jWKSs7IpMfTdaVp+Ox8101p36y0vHLX5S994i9eko9m/CPfz99lOcZ1I6vxhdpkI7ga4yNYqvWMW7JbRv28TeIT7H+HamZwb+MZOgLertdI7XVtyweTt3hUW7FUXhn3ovfscY8TklmgfxfpRJnuSyj/3nFvC9sIIIAAAhlbgEBpxn6+3B0CIRfQYOj06dM92vHjjz86x9js2LGj2W3e4yCbAg0kjBo1yiPAoIcOHz5ctC5fy4oVK+See+7xeohmun7++eeSN29er8ekdIdm2WqQVCcnsFs0EPz222+LBg01W1SzNzXDwjHBles5ffv2NSd4ci1LL+s6Tqx2sV+/fr2cOHFCatasKfXq1QtJhqw/ZodnPSFnt6x2Hpo1d5SU6LHYuZ0WK7Fft5CEk6csl8rf7FHJW+sRS1mwNtzHRi3Y6iXJXaW9xH57s0e73NtQ4uEZkjVXEffidLftT8C0X9sBUivG99iskyZNkhdftHZJb9SokceMz+EE5C1DtNL7n0rempeHIEkwvjzZMWywnFy6yLbpGiSt+EZ/Y9iGtM+Et21QBiu8ue9iiTsVH/Bd6SQ9Q7rVTLdjkp42sjEHfL9JFvx+wOe9t24YIz3bVfQ7K9I9UHp9rSIy4sFr5NS5i3Lf8BWy//BZn9fTnY/cWkEevqm87XFL/joib47dKKdO+/fMenWoIl2alLatK6nCS0Z8fIcRLF+344QcOH5O8uWOlNJGdmyF4rklJn9OZ8bqNwt2yqdT//Go7uV7r5IO1/kej9jjpGQUHD161Pn3oevpAwYMMHvhuJaxjgACCCCAQDAECJQGQ5U6EUDAFDhw4IAZ7LPj2LZtmzMgNnHiRHnpJaPLrh+Ldvd+/fXXpWLFih5H62zwdt21XA/8448/5LbbjNnCvSwapBwxYoTtH+leTgm4WLuVv//++7bnaRBXxy3U7v3uy2uvveYRLL3jjjvMiZ3cj2U79QUOTesu53ZcyfbLZgy9ENNtTupfyEuNZ/6dJUd+to6XGZHDyGp97DfjjLTpk3l03nNy+i8je/X/S77ru8uFgxvk7NbfHUVeX8NpsimvjfRzR1LBUn8CpR999JH55Y7rJbt27Wp+SeJaFk7rZ3fukM0P3efRpGwFC0upZ1+S88aXOge++1ISz53xOEYLCJLasqRq4ehFu+Q/U7YGVGdKgm8BXShIB/8Te1qe+HhNQAHi+1uXkydvqegMDnprmnugtH71QvL+w7XkgfdXy1Zj7FJ/lsduqygP3VjO49CFfx6SV774w6M8qYKPjKERGlT2f6xUzSD+dPa/Mm7uTvGWTZwnd3bp2LSUdGteVkYv3Clfz9zu0YwFQ5tLLiM7OS2Wa6+9Vtx7FPH3TlrIcw0EEEAAARUgUMr7AAEEgiagM7O/++67HvW3atXKzAp17IiLizOzCR3bvl4dmai33367mYnoeqyO96f7k1o6dOiQ5Lig2u1LhwdwDAGQVJ3+7t+5c6c0bdrU9nAdr1SDpFm8DET2zDPPeExA9fDDD5uBY9sKKUxVgYP/7Sjn9+1w1hlZtIQU63RlzFLnjmCsXLooe0c1NQJQFyy1F7z5RcldzbOLouWgVNxwzyjNXqSYMV6qNYMrS7YIKdi6nxybM0AS4y86r569SHEp3vkn53ZGWHEGTDVO7dKb2Z+Jney++NAvgfTfdDgvG+/vLBf2eu9q7K3tBdreKWV7PRe2maT79u0zM/c1y12HaNFxqPX3vw6/ouNTJxrDCegxuk97Nuh41foaHR0ttWrV8vqloDePYJXrOJc39V7kNSDmel3tFv6hMX5pvUr+B91czw+H9WPGmKvtByyTM+cSAm5OtfL55JPH6/gM/rkHSnWiq1oV8plDGHi7oLpqQDK78buwQHSkfPVsPY8MVs0kffHzdd6q8FleywiS6jiy/iwaRH7hy/V+Zb5qffmN9l5jjFO7eJ11HOyq5fLJd8Z9pNWivW5++eUXy+V0vHi7HkqWg9hAAAEEEEAgFQQIlKYCIlUggICngI5fqd1I3TMC9MiBAwdKt27dLCf169dPvv32W0uZ+0aZMmVk8eLFZiDRLmiox2/fvl0iIiLcT7VsazdvHRvQ19ifeoJ+CH7uuedEs7zsMjwtlfq50aNHD5k3b57H0Rr4/eCDD7y2XT8c6DAG7otmpt51113uxWG1rQEGfR/s3bvX/NFxSnUcxsqVK5sTfWmQQQMSoZiUKhCo/eNvM4KC+52n5ChZXop2mOjcDubK8aVvy8nV1i8BQtH1393A/Z41SFqs02hz4qYTq96XuGVjnYdE5MwhJR+9ko3q3JGOV5yBUpt7SCpYqrPbz5xpHbdVM+I1Mz6cl4PTf5K9IzwnWvHV5lIv95Uirdv4OiSk+yZPnizPP/98itqgGcL6ezwclrGLd8uHP3iOMenaNg3mfdarrjHh0OXJ2Fz3paf1Rz/+XdZvPZbsJuu4rF/2rCsGh+3S7JWFcs4IPjuWaGPMU7uhDcqWyCM9b60kjaoWNIcv0IC1ESuVPDk9MzCXbzkqvT5Z66jS8lrFmACq122VpG7FAqJjzg4xJpNyn3hLn91v77ZMMht2x6Ez0mXwcr+C5pZG2GzcZIzx+nbXtJtkTrvZf/HFF5aW6N8JOr45CwIIIIAAAsEWIFAabGHqRyCTCkybNs3rjPX6h657pubhw4fNTEudCd7bouP59ezZ09w9dOhQ+eSTTzwOXbZsmZQoUcKj3L1As4I0UOFrMiXHORow1exS7RafkoDp2rVr5c4773RUa3nVDFatXyejcs0o1fFJR44cKaNHj7Yc79jQjAu7YQgc+4P1mpCQYI4rq8FPzawqYHRDz5Ejhxmo3rVrl+zfv9+cqEsn67KbeMuuXepcqVIleeqpp6R169Z2h4S0LHZ0K0kw3jeOJUeZylL0znGOzaC96kRJsV8bARiXjEW9WOHbB0tUuZuCdl27iu3GSHUclyWrESTtfDlIqmUXzxySfV+0dew2MgkjpNSTK5zb6XllfewGmbB2vGyJvTIUg939+OqCrxnkixYtspw2bNgw20lMLAeFeCPRGGN5Q4fbvHavd21eRM5cUvGdDyRP9equxWG1rlmi1113XYrb1Lx58yS/7EvxRQKo4N1pW2XiL94zfzVA+PUzvic/DOByITlUsyXvG2o/IaJmQFY2JnE6fyFRYo0Jnv7cdtxrG+9oUkr6dKhqu/+OgUuTzMbUIGL/LtVFA5hJLRpAbdNviSX46jjnwTbl5bFWFTwCoPcb3fz/NsYVdV1+eusGKZrP++zzep0Og5fJUWNCq9RYbq4fIwPvS7t/x999951tbxnXYZtS476oAwEEEEAAATsBAqV2KpQhgECKBey6xmul7dq1sw1w6j6dtV4zOL0tCxYsEB1DVBdvf0SPHTtWbrjhBm9VWMo101GP1wmR/Fk0uPvss8+aE0YlJ2CqQV4NIPtaYmJizFns8+TJI6dOnfLIOHM9VwOr77zzjmtRmqxrYOGhhx6STZs2Be16GnTQAHFynIPVKPcgYc5y1aXI7b6zoFOjLQd/7CLnd1nHHMxepKjRjX16alQfUB17R14niRcSbM8pdGt/yVXBmjW4d9QNknj2ygf10s+sNM5NOphge4EwKdQgaf8Z1rFivTXNV1ap3RAgmin/wgsveKsubMoPzZohe95522d7NEha6YNPJbfx5Uc4L1u2bJGbb745xU3UXgqDBw9OcT2pWcHCjYfllVHrbatM667Uto1IYWG/8X/J7BWxHrXc27KMPH97ZUu5ZlcOmrRZ1m+58mWX6wFv3l9D2lxb3LXIXH/wwzUeGZ2uB2kG6LfP1veakep6rK4PMjJEpy7Z414sT99VWbo1K+NRrhMw3TVoqcQesk4atXRES5+B2ac+WyurNx31qE8LtHt92wYxEpk9Qk6eTZBjJ+Pl2KkLctyYBGz73pO25zStXVTeecD3BHW2JyazUL8E1u737ovdF+3ux7CNAAIIIIBASgUIlKZUkPMRQMBDQGcx99YFUbvXaxDMbrlkfCLQLvlLlizx2K3ds12DjN7+iH7rrbdEJ3wKZNEMSJ1Myp/sUq3X0SVfPxjruj9LamUtOa6lQVsNHGs2Z1ovgwYNks8++yzol33kkUf8DmIHvTHGBfZ+0UQSz5xzXiqqYi0p3M7aNdC5M5VWLhzeKAfGPeBRW7Eu30hk4bTrBulowJ7/NJBL2p/Ubcldo6kUvNFzPGL3MU2LdfpCIovWcjs7/Wz66mpvdxeTe1iHS3A9Rr80ch/+49Zbb5WPP/7Y9bCwXd87+ls5+M3nXtunEzdVGjDI6/5w2aH/3+nVq5fH+M+BtE8DOvqFWzgOHzJ0yhb5YdFuj9vRAN/o5+p7lKengtv6/yYHj175naxtb3tdCXnj3mpeb8PbsAQ6nujCYc0lm1tW6Kvf/SkLfj9gW5+e8+MbjaVwXu+Zna4narD23reXuRaZ6y3rFpfB3Tx/n19ISJQ3J2yS+auvDPmiJxQvHCVT+zb2qMdR4GuSqK6tysnjrSuYwwM4jnd9fe6r9bL0j8OuRea63usiw8efrFmPk5NRoF/E3nLLLR5n6t+X+fPn9yinAAEEEEAAgdQUIFCamprUhQACoh86NVC5cOFCDw0N7q1cudLnh0ntEt+9e3fLRE0ajJwyZYpUrVrVWaeOeamzorovOr6fjvMX6KLZpTr+qQZy3ScQ8FaXtkszwDS4m1TAdMKECWb3fW91BVKu19Ku+NpNPxSLjtlqF8xO7bb4OzlXal/XW317P2ssiefjnbujKteVwm1GOreDsbJ//K0ekyWlVSar+/0kxp+VvZ96TkSWLW8eiXlQJ93wzBR1H6c0ulEnydcg/DMm3e/d3672ruf5yibV4zp27Gj+PnQ9R8fttRvD2PWYcFo/sWa1/PtyL9sm5W14vVQaNMx2X7gVnj171vy9r2Mna4bp8ePHpVSpUlK6dGlzOJAqVapI2bJlJXv27HLw4EHzmB07dsjJkyelYcOGYTORk53rX7vj5MF3V3nsqlgqr4x7sYFHeXopiL94SW54wTrZj7Z99MsNpYoxXqivZYCRWfrzb3s9DrHLKh0+datMWmA/hMErna6S9o1KetTjraDPmI0eQU/HsRq4vr1hCalUPLdky5pFVmw9Kt/M2iHxRrDUfdFgZ8+2Fd2LndsvfbPBYzIm3TnskVrSrEZh53HuK3uNIQra91/qXuzc/vjpa9Ns4i8dsqdFixbOaztWtm7dKpGRkY5NXhFAAAEEEAiKAIHSoLBSKQKZV2DSpEmiY4naLdqtXruuJ7Vol3Mdp1LHv9SxKjXwWqRIEY/T7AINGoxds2aNZZxPjxOTKNAPwBrY1O79vsZMdVSjgUu9Zw0gevsDvnfv3jJu3DjHKearBkU6depkDkVgN+mV5eD/b+isr59//rk5E7Pd/rQou+mmm0Q/rAR7adasmfkMgn0df+vfO9IIlF5wCZRe1UgKt/rI39MDPu7croVy6MeXLOdlMbKdSvSYIRFR3j/sWk5IxY0LB9fKgQmPetRYrPOXElnkGo9yLTi/f5UcnPikc1+OEuWk6N2TnNvpYSXQLNIqMdWlU53OUivGdzdVzZieM2eOB8GqVaukaNGiHuXhWrD3m6/k4OgvPZqXt4ERKB2cPgKlHo3PYAUtXl3kMSt8+ZJ5ZcJL6TdQ+ueuOOkxwhoA1i7ls/s3SfLpaabmza8t9hgntPE1heW9h6wZ798u3CWf/Oj5/zu91sy3mvjd5f746QvS+rUlSbYtqQMK5s8h016/3mtG6EUj47/Zyws9Aqz9ulWXdnVjfFb//Nd/yG/rD3k95s6mpaR3+ytfWHs9MBV2eOuFs3PnzlSonSoQQAABBBDwLUCg1LcPexFAIAABncCnZcuWXoOLv/32m5mlE0CVPg/98MMP5d13Pbv7zpo1S6pV8971zmelLjs1SKrBUp3N2J+AqQYxtdtsuXLlXGq5vKpdyNzH9NTu/k8//bToxEjajX7MmDG2mbgaiG3Tpo3ceOONZoZFqMftfPPNN+Xrr7/2uMfUKtAA8h133CH33XefFCxYMLWqTXE97t3Oc9doYnQ3H5Hier1V4D4mqh4X3fBeydfQ/osIb/WkVvm5vb/JocnWLzqiKtWWwm1Heb9EYoLs/s91zv3pbUKn5ARJB7cd5LxfXyv65Yp+seS+hNPs6e5ts9s+Y2R+/f1IN49deeo2ksrDPH8/exxIQdAF7nzbc4zLWpULyOdPefbKCHpjUukCP67cJ4PHWcfJjikSJT++5r1Luuulv5q/Qz77aZtrkW2X9ulrYqX/aM9J27yNKWqp0GVj3JLd8sHkLS4lga9qt/fPn60nV5eJ9nqyZqI+8/Fay/6yMXlk4isNLWXuG79uOiIvfLbOvdiy7W8g2nJSMjfsMkr1byHN+mZBAAEEEEAg2AIESoMtTP0IZBIB7TKvGZXuY+45bl8DqKkdXNPAq44T6r68+uqr8sQTT7gXJ3tbg6Q66dP777/vV8BUu8U3bWrtomw3udXdd9/tEejVoKkGnHW2e10vX768lChRIkUZssm+cS8nasZu+/btvewV0QmprrnmGqluzHStQU8NHBcoUEAuXrwo8fHx5s/58+flzJkzcvToUbP7akREhHmedm/V7q7htiSej5O9n91oaVbuq1tKwZZDLWWptXF6yxQ5OssacIvImUNKPqoZSVlS6zIB1ZMQt1Niv71b5P9DlJrZrY/MlYgc+XzWs+/LZnLx9BnnMUXufEdylmnu3A7HlWB0tXe/Tx0i5NNPP3UvNrvkDx8+3KM8nAv+7NTBGCLCOo5i7joNpMrw98K52ZmmbZ3eWekxSY+vmd7TA8wHP/8j4+Z5ZheOfKau1KmQP8lbsMtI1UCkTpLkuiz7+6g8+6k18Kj7Zw40vijLE+l6qM91u7FOG9QoJGv/PuaR/WlXkQYpRxn3VqZwLrvdzrIpK/bJkPHWAHKvDlWkSxPv/189c/6itHvjV4+sY2elLisT+lwn5Yv6boPL4cleXbZsmdnjxrUC/dvC37HkXc9jHQEEEEAAgUAFCJQGKsbxCCDgIaABPZ2ExD1j0vVADZJqsDQ1Fx0fTrM43Rd/x7bcvn27lClTRrJmzepehe22Bva0+/yIESOSDJjOnz/fHNvOUZG3brbpcWICHYd22LBh5pABen+a5XHPPfeYEy9ocDQ62nu2i8Mj3b0mxsvuj41MJZd5jIIZKN37uTFb/Lkrs8WrV+E7hklU2RYhpTOHA5hqDAdgOEQ3vMfIbrUODWDXuLM758vhqa86dxVq95bkqtjWuR1uK8nJIvWnq737fWq2+uuvv+5ebP570i8jQp057tEwHwWbn3pMzm7+03JEdNMbpeIb/S1lbIRGwC5Q+kz7KnJfU+/Bs9C01P+reusmrsHOT3teK7XK+w6WHjVmeW/T19oV3i5Q6m9ANamW3zFwqew/fNZy2LL3bhTtKj933QEZZ0y4tdUYTsB10fZUK59PWl1bTG6rFyO5ciT9t8oPy/fK0AmbXasRX2OLJhjXf+zj3+XPbcct53jb6Na6nDzdpqK33alWruPSuw/V1KhRI/n+++9T7RpUhAACCCCAgDcBAqXeZChHAAG/BXRMTx1P0tuiWQCa/elvQNJbPXbldjNH63EaaChc2Ps4jr/++qvZtVvHNNUg77333is1atSwu4RHmQZMNcP0vffe8xow1QDszz//7JyVfujQoc7AomuFjz/+uOj4pelx0TFkNfu1du3aQXm24WYSO6a1JBgZsI6l4M0vSu5q9zo2U+315Iav5fiCTyz1hdPYnheO/CWX4k9LjuL1LW30tXH4pwfk7PaNEpErp5R8eLFxaGiyYn21Uff1ntFHtsT637UzqQmbfF1v7dq1cuedd9oe8sknn4j+bksvy5YXn5PTa1damlukywNSqscjljI2QiNwozEe56nTV8ZX1lYMf7S2NKleKDQNSoWrdntvlWzZaQ0sulbb5aay8qQR0MtuTIxkt/yy4aD0/nKDZZfO7P7rcOuXUZv2nJQHhlvf23YBVUtFNht3D1kuu/eftuyZ0LuRlC+W21lmfAcpB0+ck31Hz0mhfDmkdMEoozeJc7dfK5OX7ZVh31sDpWOMCa4q20xwpdd7/mv7We5zGkHZZ41gunt2ap7c2WXewKYBt8uvxrscpL8D9e8m10V7EA0ePNi1iHUEEEAAAQSCIkCgNCisVIpA5hLYtm2bz2zRfv36SY8ePYKCon80jxzpOfO4BjF9dQ/v3LmzLF261NKml19+WR577DHJli2bpdzbRlxcnBn8tOs+q+cMGDBA7r//fvP0yZMny/PPP29bVTB9bC9IYbIE4o9ukRMr3pWLJ/ZL9iIVgzM+qTGm597PmkhifMKVNhoflGMenCbZ8sRcKUt3a5eMibBOSURk3rBsuXa17z/DM7vTV2P7tR2Q5IRNvs7XzGzNstex+NyX5s2by7fffuteHLbbdoHSUi/2kSJt0k+wN2xxU9iwk2cT5KbeizxqmfR6YylTKMqjPL0U2I276t726DzZ5a2uNaTxVdaA8Ja9p+SBd1ea2Zyu5+TKmU0WDLF+6avHdntnheth5vovQ5tLbj8yPB0nPm50319rdON3XW6/oaS8dvdVrkUpXp+0dK8Mn2gNlDarU1SGdbdOLnfwxHl54as/vAabB/eoaQTSi0izlxZ4OA17pJY0q+H9i+gU34RRwaOPPiqzZ8+2VNW/f3/p3r27pYwNBBBAAAEEgiFAoDQYqtSJQCYT0K73FSt674r1xx9/ODMrU5vG23iZmo2lGQneFu3CFRsb67G7Xr16MnDgwIAmg/I2qVTr1q3NGer1ImqkExR5G8P17bffNjNcswSQPqJ1ajavBlr0XjRw27hxY6lbt67HfVGQPgSOLx8qJ1f+19LYPHXaSoEmb1nK2Eg9geR0tfd3wqakWvnll1+Kfvi3W5YsWWIODWK3L9zKNna9Vy7E7rE0q8K7/5F8tetYythIe4Glm4/IcyPXWS6smZOL32nh94ztlpPDZKPNm7/K0ePn/WpN8cJRcq0xeVViokjs0bNmN3Pt8u6+tKxbXAZ3q2Ep3rrvlHQd5hko9XcsVEdl707bKhN/2eXYdL6+/0Qdua5qQed2SlcW/3VYXvp8vUc1FUvllXublZYc2bLIBiMTd8riPR4BUMdJHVuUkRfvqGxuPvPFelnx52HHLvO1ujEG7NfGeKnBWnQ885o1a3r02Jk+fbrtcEvBagf1IoAAAghkXgECpZn32XPnCKSqwAcffGCO3eleqU6qpJMrBWvRP6jr168vR44c8bjE33//LTlz5vQo14KbbrpJtm7dartPCzXQesMNN0itWrWkWLFikj9/fjPTVLPA9Jp6PR2TVWdg1bEG7YKu7jO0+upqq9fUIG3fvn1Fx1j1tuzbt09Wr14tixYtkpkzZ3p8kNBrajBWJ0diSV8COmHUvlE3ySWXD/ARkdkuT+AU4V+Wc/q649C2Ni0mbErqDvXLDf3SRieMc1/094GOx+dvhrv7+Wm1nWC0fcPtrTwuV33cZMlRrLhHOQVpK/DJrH/l21nbLRetYwTmRhoBuvS83Nx3scSdujKcgHaHz50rm6UskPvT86cPuEEK5LZO0LTN6C7fxeg27750bVVOerb1/gWx+/GbjS783d268Osxet3PetWVmmV9T4rnXp9unzgTLyeMjOESBXJKNqMeXS4kJEqTFxeY68n5z11NS8krd1V1dq2fu/6A9P3aOv6w1jup73VJTiyVnOvrOZs3bxb9otl90d5L4f770L3NbCOAAAIIpE8BAqXp87nRagTCTuDs2bPSokULS8BQx+nUAfl9jRWaGjeiGaCjRo3yqErLu3Xr5lGuBXZd720PTEFhhQoVZMEC6weW1157TcaMGeOzVg3OajaFZulq4FPHAtVJn1atWmUbUHGvjA8T7iLpY/vI3F5yZpN1OIiCrXtL7qrt08cNpKNWBtrVvkpMdUnOhE3+kHib1EnPfemll+Tpp5/2p5qQHRNn/G7a9vyTlutni84vNSf/JMY3NpZyNtJe4KVvNsjidQctF36gTXl5onUFS1m4bOgkS5oF+9WcHc7Jj269vqSZ4RhpZMI6FvdxVwvmzyHfPV9fuhrByONxFxyH+fWqGbYjHqstDYysU/dl+4HT0mmwZ6D0loYl5K3O1dwP97n91vebZMayfR7HaLC0041l5JGby0tUZFaP/Vqg44lu3B0ny/4+Imv+OS6bdpyQc8Zs9brEFImSH18zJhv8//Lgh2vkr3/9m5zJcY6+epvgq4thus0I9LoudxoB1d7tq7oWpdr6Rx99JMOHD7fUp8OU6KSgLAgggAACCKSFAIHStFDmGghkEoF//vlHHnzwQdm1a5c5OVKfPn3MTMxg37637vcaZNRJmwoW9OzWpl3WO3XqZAnspnY7dUzSXr16WarVzDEdC1UnegrWQqA0WLLBrTd29M2ScOzKh9usuaOkRI/Fwb1oJqw90K72KZmwyR/e+Ph4c6xS/b1pt+jvCv3iJFyX/ZMmSuzIDyzNi25+s1R8/U1LGRuhEXhy5FpZs9k6NuYHT9aRRlU8/78YrBZqkO/4mQtyJC5e9h8/J7HH9Oes0Q3+vBw0to/EnZcTJ+PlzDmXsZndGpM/OlK+eraelDQmONKlxauLLMcXLZhTfup3veiYrC98/Yes33LMrQb7zbLGJEcfP15bikTnsD1Au+jf2v83j27+T95ZWbo3L2N7jrfCU+cuyi1GJmy8kfVpt+gESk1rFZWqJfNICeN+4ox7+Wv3Sflz+wnZYQwBYDdcgNajgdalI1o6q5z/x0Hp89UG53ZSK3r+oIdqSvOri9geutcYrqDjwGWW699Yr7gMMsZ/Te1Fv3TX4YPcs+yHDRtm/l2Z2tejPgQQQAABBOwECJTaqVCGAALpTkC7ytuN/6kD/3sbA1BnbNcMz3nz5qX6/Wo26dixY6VEiRIedWv3fc2A1XFJU3sZMmSImS2b2vVSX/AF9o9tI/FHrowFV/i2tyWqvGeX5uC3JGNeIdCu9sHMInUXnjNnjjzyiP0M8fq7ZMaMGRIVdTlA5H5uqLd3jBgux6ZPsTQj5olnpfjdHS1lbIRGYPAPf8uPxniUrsuiYS0kZ+SV7EzXff6svzF+k8xdFStVy+WTwkYAUxcNtmkgTwOVcafj5bQR9NTA51kjOOgtMOjPtVyPeeTWCvLwTeXNIvdAqXtW5bglu+Wzn7c5sy5d69FJm6qWjZauRqDzhmqFXHfZrruP86r3OnNgE8mXK7vt8b4K12w7Jr0+XZdqJnot90CpltlN6qTl7svN9WOk120VvQaKHceP/3WPvP/fvx2bZmD1xmuKOrdTa0X/NtLeQO7L77//LoUKJf2s3M9jGwEEEEAAgeQIEChNjhrnIIBA2An89NNPXruo6liiuXLl8trmWbNmyZtvvplq2aW33nqraPaDZrT6WpYuXSqadWo3vqmv8+z26TV1lljtts+SPgUSzx2TwzOflMRzpyS64VOSq8It6fNGwrDV4ZZFakfUo0cPr1/a6L5+/frZnRbysu1v95fjv1hnp670wWeS9+qrQ942GiDy48p9MnjcJidF6eK55b+vNnJuB7rSb/xfMntFbKCnpcrxvbtUkzsbXP7y0b3rfdmYPDLxlYaW68RfvCSb98TJrsNnzC7tmjVatmhuiY7KZjnOn43/zNwmo2fvMA/t1aGKdGlS2p/TbI85YGTRPvHJWtl78Izt/kALX+l0lbRvVNLjNM3c/XjGv7LZ6LJ/yFjXJTp3dokpFCXlY3JLNyNQXOr/GboeJ9sUaBB8npGtevbCxRTdv03VZtG4ceOkd+/eHrubN2cEKDQAAEAASURBVG8u3377rUc5BQgggAACCARLgEBpsGSpFwEE0lxg4sSJ5ph+rhfWYOWGDRska1b7cb8cx+oETcuXLzcnT5k6daqj2O9XDVC2bdvW7EJbpUoVv8/Tmevnzp0rn376qTkOqb8nNmvWTK6//npzlnsdy9RXINjfOjkOgYwoEGiQtF/bAVIrJu27uutEbdddd53XR6Dj8+k4feG27P3qCzk41jp2YK2ZCyQi8nKmYbi1N7O1Ryf3ad13ibObes+7KkvXZoF1GXeYeZvYyLE/mK86jui8Qc2cmbDukzmVL5lXJrzUIJhNkHMXEuXCxcRkBVrdG6ZB3HenbpFpv+61dGl3P87bdh4j4NnIyIZ96KZyUtEIfqfnJTExUT7++GOPcUkd9zRy5Ehp06aNY5NXBBBAAAEEgi5AoDToxFwAAQTSUmDdunUyYsQIc1Z4vW7Pnj3lxRdfDKgJOmagzmavEyjpuIE6w/3x48fNmeTz5s0rxYsXN7vU62uxYsWkVKlSqdIlbOPGjaLjvO7cudP8OXTokDHzbBbJly+feT3txq8B2Ro1ajDza0BPlIMzo0ByutoPbjsopFS+JnbSL30mT54s1aoFNoFMsG/oyKKFsqv/a87L5Lq6tlT94GPnNiuhF1j9zzF5Y+xGKVkkl3z8WB3JnvXyDOmBtuzD6f/I2Lk7Az0tyeO163iE8aOLt276XW4qK71ureSsyz1QWrlMtIwxJnNKb4uOWzpuyS6ZtGi3xJ2K99p8HaP12soFpWHVAuZwAYXz2o+p6rWCMN1x4sQJ8280HX7EbqlXr57897//Nf8WsttPGQIIIIAAAsEQIFAaDFXqRACBkAtcuHBBNEs0XMf1CzkQDUAgAwsEmkUa7Amb/KXWzKrOnTub2e1252iw9KuvvpJGjZLfddqu3pSUJeoXS906Sfyh/WY1JZ9/VYq2uy0lVXJumAqMXbxbPvxhi9fWacCzQL5IKW505y6YN1IKGsE9Hcczv5H9mP//r3mNbu96nGPJZ+xzTNCkZf/uPy093l/tzIDVsuKFo2TcSw0ltzHZkWNp3W+JZXZ7HS/1O2Oyp/S8aJbpHmOYgJ2HzxoB44uSM3s2KZY/h5Q0usq73nt6vkfXtuuwSDoBqK/hh6ZPny5XM4yHKxvrCCCAAAJpIECgNA2QuQQCCCCAAAIIpI1A7xl9ZEvsX35dLC0nbPKrQcZBe/bsMYfV8HW8Zl9VrVrV1yFpuu+UkYG/+/3hElmqtFTo87pkyRb4GJBp2mAuliyB0+cvSvu3lzoDlAWNIF7tigWkQZUCUr9ygYDGu/TVgMMnz8sgY+KgE0aGZV2j3vualvaYOKnNm79aZqLPCIFSXyYZbZ/2oNHhinwtn3zyiehEnSwIIIAAAgiktQCB0rQW53oIIIAAAgggkOoCgXa1D5csUjsIX5PT6fFNmjSRMWPG2J1KGQJBFdCZ7fcaEwPlMzJDkzPre2o17p6hK2Rn7ClndQRKnRTpYqVr166yZMkSr23Vme+7devmdT87EEAAAQQQCKYAgdJg6lI3AggggAACCARdIJCu9uGYRWoH1LdvXxk9erTdLrNs0qRJ0qBBcCev8XpxdiAQYoEnR66VNZuPOltRsVReGfci/x6cIGG+0r17d1m4cKFtK8kktWWhEAEEEEAgDQUIlKYhNpdCAAEEEEAAgdQVCKSrfThnkbqr6KRymnW1fPly913mtk5a16FDB9t9FCKQ0QX6jf9LZq+Idd5mrpzZZMGQZs5tVsJb4JtvvpE33njD0kiduKl///7mhJWWHWwggAACCCCQxgIEStMYnMshgAACCCCAQMoFtKt9/xmv+11Rv7YDpFZMTb+PD4cDdUboxx9/XJYuXerRnHfeeUfuuecej3IKEMgMAp/M+le+nbXdcqsLhjaXXC4TPll2shFWAgkJCfL111/LgQMHJDo6WurXry/XXXddWLWRxiCAAAIIZF4BAqWZ99lz5wgggAACCKRLgUC72g9uOyhd3qc2WgMKPXr08Oim+v3330ujRo3S7X3RcARSIjB73QHp982fliqGPVJLmtUobCljAwEEEEAAAQQQCFSAQGmgYhyPAAIIIIAAAiERyEgTNgUCeObMGXnwwQed3fALFSpkToSSO3fuQKrhWAQyjMD2g2ek06BllvtpWruovPNA+soat9wAGwgggAACCCAQFgIESsPiMdAIBBBAAAEEEPAlEGgWaac6ndNdV3tf96/B0rFjx8qpU6ekTZs2ctVVV/k6nH0IZGiBi4mXpPHzv1juMXu2CPnFGKc00nhlQQABBBBAAAEEkitAoDS5cpyHAAIIIIAAAmkiEEiQNJQTNmnGq/uS3sZFdW8/2wiEq0CX4Stl256TluY9fntFebBlOUsZGwgggAACCCCAQCACBEoD0eJYBBBAAAEEEEgzgUC62leJqS5JZZHaBTL1Zjbs8wxwbjxgHf/Q9aa3xP7luulz3Z92+ayAnQggYCtgN6FTTmMyp7lvNyWr1FaMQgQQQAABBBDwR4BAqT9KHIMAAggggAACaSoQSBZpwTyFpXDeos72BRLIdJ4UxBUNlqbnCaWCSEPVCCRbYOu+U9J12AqP8wf3qCkta175feBxAAUIIIAAAggggIAPAQKlPnDYhQACCCCAAAKhEfArUJrFaNul0LQvkKuGcjiAQNrJsQikN4FO76yU7Xut3e8HPHC1tKpdLL3dCu1FAAEEEEAAgTARIFAaJg+CZiCAAAIIIIDAZQHtIt9/xusZhoNAaYZ5lNxImAms2HpUnvl4raVV3792nZQrkstSxgYCCCCAAAIIIOCvAIFSf6U4DgEEEEAAAQTSRKD3jD7itft8OskidUARJHVI8IpAcASmrNgnQ8ZvkqwRWeS5u6tKx8Ylg3MhakUAAQQQQACBTCFAoDRTPGZuEgEEEEAAgfQhkNGySSf3+DF9wNN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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, there is a significant difference between the prediction and the result, which we refer to as the error. To measure this error, we use something called a loss function. The main goal is to minimize the loss function as much as possible, ideally getting it as close to zero as we can.\n", + "\n", + "One common way to calculate this error is by using the Mean Squared Error (MSE). First, we measure how far the prediction is from the actual output using this formula:\n", + "\n", + "$$ \\text{Error} = y - y_{\\text{pred}} $$\n", + "\n", + "Then, we square this error to make sure it’s always positive:\n", + "\n", + "$$ \\text{Error Squared} = (y - y_{\\text{pred}})^2 $$\n", + "\n", + "Finally, we sum up all these squared errors across the dataset and take the average to get the Mean Squared Error (MSE), which is one of the most popular loss functions out there:\n", + "\n", + "$$ \\text{MSE} = \\frac{1}{n} \\sum_{i=1}^n (y_i - y_{\\text{pred},i})^2 $$\n", + "\n", + "![mse_loss_explication.png](attachment:mse_loss_explication.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*You only need to set up a squared error since we’re learning example by example, so there’s no need to sum anything! But hey, as a bonus, you could try implementing it in a **mean_squared_error** function :)*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Define the loss function \n", + "def squared_error(y, y_pred):\n", + " ...\n", + "\n", + "#TODO: Test the Squared loss function\n", + "for x, y in train_set:\n", + " ...\n", + " error = ...\n", + " print(\"Error:\", error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### step 6 : Calcule the derivative \n", + "\n", + "After get this loss we need to reduce it near 0 ! To move from just measuring the error to actually reducing it, we need to figure out in which direction (+ or -) and by how much change our parameters `w`and `b`. This is when derivatives (the slope of your loss function) come in !\n", + "\n", + "Why Derivatives?\n", + "\n", + "•\tA derivative tells us how fast the loss changes as we vary a parameter.\n", + "\n", + "•\tIf the derivative is large and positive, it means the loss will decrease if we move the parameter in the negative direction.\n", + "\n", + "•\tIf the derivative is large and negative, it means the loss will decrease if we move the parameter in the positive direction.\n", + "\n", + "For a single data point (x, y), the derivative of w is:\n", + "\n", + "\n", + "Derivative for `w`\n", + "\n", + "$$\n", + "\\frac{\\partial L}{\\partial w}\n", + "= 2 x \\bigl(y - ypred\\bigr).\n", + "$$\n", + "\n", + "Derivative for `b`\n", + "\n", + "$$\n", + "\\frac{\\partial L}{\\partial b} = 2 \\bigl(y - ypred\\bigr).\n", + "$$\n", + "\n", + "*Bonus : To better understand and apply the derivative of the function MSE with the linear regression, try calculating it manually and use the principe of [chain rule](https://www.youtube.com/watch?v=NO3AqAaAE6o) ! :)*\n", + "\n", + "chain rule : \n", + "\n", + "$$ \n", + "\\frac{\\partial L}{\\partial w} = \\frac{\\partial L}{\\partial y_{\\text{pred}}} \\cdot \\frac{\\partial y_{\\text{pred}}}{\\partial w}.\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : setup the derivative of the loss function\n", + "def derivative_w(x, y, y_pred):\n", + " ... # 1 line of code\n", + "\n", + "def derivative_b(y, y_pred):\n", + " ... # 1 line of code\n", + "\n", + "def derivative(x, y, y_pred):\n", + " return (derivative_w(x, y, y_pred), derivative_b(y, y_pred))\n", + "\n", + "\n", + "# Print the result of the derivative\n", + "for x, y in train_set:\n", + " ... # here\n", + " derivative_weight, derivative_bias = ... # here\n", + "\n", + " print(f\"x: {x} ; y: {y} -> y_pred: {y_pred:.3f} | Derivative Weight: {derivative_weight:.3f} | Derivative Bias: {derivative_bias:.3f}\")\n", + " # save the derivatives for after \n", + " dw_values = [derivative_w(x, y, forward(x)) for x, y in train_set]\n", + " db_values = [derivative_b(y, forward(x)) for x, y in train_set]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot derivative of w\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(range(len(dw_values)), dw_values, marker='o', color='blue', label=\"Derivative of w\")\n", + "plt.title(\"Derivative of Loss with respect to w\")\n", + "plt.xlabel(\"Index of point in dataset\")\n", + "plt.ylabel(\"Derivative (dL/dw)\")\n", + "plt.axhline(0, color='grey', linestyle='--', linewidth=0.8)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Plot derivative of b\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(range(len(db_values)), db_values, marker='o', color='orange', label=\"Derivative of b\")\n", + "plt.title(\"Derivative of Loss with respect to b\")\n", + "plt.xlabel(\"Index of point in dataset\")\n", + "plt.ylabel(\"Derivative (dL/db)\")\n", + "plt.axhline(0, color='grey', linestyle='--', linewidth=0.8)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "As you can see, the larger the error, the larger the gradient will be. This is because the gradient measures how much the weights and biases should change to reduce the error. During training, this gradient is applied. What this means is that at every iteration, the gradient is used to update the corresponding values (weights and biases) so the function can learn and improve.\n", + "\n", + "This process is called gradient descent. It is the step where we adjust the weights and biases based on the gradient using the following formula:\n", + "\n", + "$$\n", + "w = w - \\eta \\cdot \\frac{\\partial L}{\\partial w}\n", + "$$\n", + "\n", + "$$\n", + "b = b - \\eta \\cdot \\frac{\\partial L}{\\partial b}\n", + "$$\n", + "\n", + "Here:\n", + "\n", + "•\t $ w $ $ b $ is the weight, and is the bias.\n", + "\n", + "•\t $ \\eta $ is the learning rate, a parameter that controls how large each update step will be.\n", + "\n", + "•\t $ \\frac{\\partial L}{\\partial w} $ and $ \\frac{\\partial L}{\\partial b} $ are the gradients of the loss function with respect to the weight and bias.\n", + "\n", + "\n", + "If the gradient is negative, the weight increases ; if the gradient is positive, the weight decreases. This helps reduce the loss over time.\n", + "\n", + "We don't add all the gradient to the weight, as you can see he his to big to reach the '2' value. To counter this problem, we only add a percentage of the gradient, and this percentage is choose by the learning rate.\n", + "\n", + "The learning rate plays a crucial role in how the model learns:\n", + "\t•\tA large learning rate makes the updates bigger, but the model might “overshoot” the optimal values and never fully converge to a solution.\n", + "\t•\tA small learning rate makes the updates smaller, which helps the model converge more precisely but takes a lot longer to reach the optimal solution.\n", + "\n", + "Choosing the right learning rate is critical. Too large, and the model might not learn properly; too small, and training might take too long, **choose carefully !**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Update the weight and bias\n", + "LEARNING_RATE = ...\n", + "\n", + "w -= ...\n", + "b -= ...\n", + "\n", + "print(\"Updated Weight:\", w)\n", + "print(\"Updated Bias:\", b)\n", + "\n", + "for x, y in train_set:\n", + " y_pred = forward(x)\n", + " derivative_weight, derivative_bias = derivative(x, y, y_pred)\n", + " print(f\"x: {x} ; y: {y} -> y_pred: {y_pred:.3f} | Derivative Weight: {derivative_weight:.3f} | Derivative Bias: {derivative_bias:.3f}\")\n", + " # Plot the derivatives\n", + " dw_values = [derivative_w(x, y, forward(x)) for x, y in train_set]\n", + " db_values = [derivative_b(y, forward(x)) for x, y in train_set]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot derivative of w\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(range(len(dw_values)), dw_values, marker='o', color='blue', label=\"Derivative of w\")\n", + "plt.title(\"Derivative of Loss with respect to w\")\n", + "plt.xlabel(\"Index of point in dataset\")\n", + "plt.ylabel(\"Derivative (dL/dw)\")\n", + "plt.axhline(0, color='grey', linestyle='--', linewidth=0.8)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Plot derivative of b\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(range(len(db_values)), db_values, marker='o', color='orange', label=\"Derivative of b\")\n", + "plt.title(\"Derivative of Loss with respect to b\")\n", + "plt.xlabel(\"Index of point in dataset\")\n", + "plt.ylabel(\"Derivative (dL/db)\")\n", + "plt.axhline(0, color='grey', linestyle='--', linewidth=0.8)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# OPTIONNAL BUT RECOMMENDED : if you want reload the weight randomly at the first value \n", + "\n", + "w = random.uniform(mini, maxi)\n", + "b = random.uniform(mini, maxi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the model is learning effectively because the gradients are decreasing. Now, we need to repeat this process for a certain number of epochs *(basically, going through the entire dataset multiple times to give the function more opportunities to learn)*.\n", + "\n", + "Try your best to implement the train function alone ! if you don't find the solution you can check just below this cell, there is a description of what is inside the train function, *good luck !* " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Train the model\n", + "\n", + "epochs = ...\n", + "learning_rate = ...\n", + "\n", + "\n", + "for ... :\n", + " for ... :\n", + " # ~ 5 lines to code \n", + " print(f\"Epoch: {epoch}, Loss: {loss:.5f}\")\n", + "\n", + "print(\"Final Weight:\", w)\n", + "print(\"Final Bias:\", b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your goal above is to create the function that will train your function ! A little help of what are the step :\n", + "- Define `Hyperparameters` (epochs and lr)\n", + "- Iterate to your `epochs` and for each epoch to your `train_set`\n", + "- apply a `forward` function to get a `y_pred`\n", + "- calculate the `squared error` \n", + "- calculate the `derivative`\n", + "- apply this derivative to the parameters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well done you create your first machine learning function ! you can test is on many example you want by simple run the forward pass :)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = 3\n", + "print (\"Prediction \", forward(x))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the result from -10 to 15 with the actual data\n", + "x_range = list(range(-5, 15))\n", + "y_pred_range = [forward(x) for x in x_range]\n", + "\n", + "# Actual data points\n", + "x_actual = [i[0] for i in train_set]\n", + "y_actual = [i[1] for i in train_set]\n", + "\n", + "plt.plot(x_actual, y_actual, 'ro', label='Actual Data')\n", + "plt.plot(x_range, y_pred_range, 'b-', label='Predicted Data')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "plt.title('Actual vs Predicted Data')\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "Congratulations on building your very first machine learning algorithm !! I bet you were itching to dive straight into AI, and here you are, well done, now let's discover another fundametals of machine learning, [logistic regression]()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day03/1 - Regression/logistic_regression.ipynb b/AI/Day03/1 - Regression/logistic_regression.ipynb new file mode 100644 index 0000000..dd347fb --- /dev/null +++ b/AI/Day03/1 - Regression/logistic_regression.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ~ PoC AI Pool 2025 ~\n", + "- ## Day 2: Neural Networks from Scratch\n", + " - ### Module 2: Logistic Regression\n", + "-----------" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you just dove into linear regression; let's discover another banger — **logistic regression**! \n", + "\n", + "While linear regression outputs continuous values, logistic regression predicts probabilities, making it ideal for classification tasks.\n", + "\n", + "The key difference lies in the **output function**:\n", + "- Linear regression: $$y = a * x + b$$\n", + "- Logistic regression: $$ y = sigmoid(a * x + b)$$\n", + "\n", + "Moreover, you might wonder: is it possible to perform logistic regression with a polynomial function? The answer is **yes**! Logistic regression can work with polynomial transformations of the input, allowing the model to capture non-linear decision boundaries.\n", + "\n", + "Let's dive into building logistic regression step by step, including polynomial transformations!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Create the training set\n", + "train_set = [\n", + " [1, 0],\n", + " [2, 0],\n", + " [3, 0],\n", + " [4, 0],\n", + " [5, 0],\n", + " [6, 1],\n", + " [7, 1],\n", + " [8, 1],\n", + " [9, 1],\n", + " [10, 1]\n", + "]\n", + "\n", + "print(train_set)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why a dataset like this you will say ? Because logistic regression works well as find cluster of data and make a linear observation of it here's what you need to understand " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Separate the training set into two clusters based on the label\n", + "cluster_0 = [point[0] for point in train_set if point[1] == 0]\n", + "cluster_1 = [point[0] for point in train_set if point[1] == 1]\n", + "\n", + "# Plot the clusters\n", + "plt.scatter(cluster_0, [0] * len(cluster_0), color='red', label='Cluster 0')\n", + "plt.scatter(cluster_1, [1] * len(cluster_1), color='blue', label='Cluster 1')\n", + "plt.xlabel('Data Points')\n", + "plt.ylabel('Cluster')\n", + "plt.legend()\n", + "plt.title('Training Set Clusters')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2: Initialize the Weight and Bias\n", + "\n", + "We need to initialize both `w` and `b` with random values beetwen 0 and 10 at the start : " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "random.seed(0)\n", + "#TODO: randomise the weight and bias beetwen 0 and 10\n", + "\n", + "mini = 0\n", + "maxi = 10\n", + "\n", + "w = ...\n", + "b = ...\n", + "\n", + "print(\"Initial Weight:\", w)\n", + "print(\"Initial Bias:\", b)\n", + "\n", + "assert w > 8.44 and w < 8.45, \"Weight is not correct\"\n", + "assert b > 7.57 and b < 7.58, \"Bias is not correct\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### step 3 : Make prediction using the known formula (with sigmoid)\n", + "\n", + "To make predictions, we’ll use the sigmoid function, a fundamental tool in machine learning. The sigmoid is often used to squash values into the range [0, 1], which makes it particularly useful for binary classification tasks. It’s defined as:\n", + "\n", + "$$\n", + "\\sigma(y) = \\frac{1}{1 + e^{-y}}\n", + "$$\n", + "\n", + "Where:\n", + "•\t $ y = w \\cdot x + b $ (the neuron formula if you had forgotten)\n", + "\n", + "•\t $ e $ is the base of the natural logarithm.\n", + "\n", + "The sigmoid function ensures that large positive values of z approach 1, and large negative values approach 0, with a smooth curve in between.\n", + "\n", + "\n", + "With the formula of above, calculate the `y_pred` of each input `x` in the train_set in a function call **forward** and it will use your **sigmoid** function \n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "e = math.e\n", + "\n", + "#TODO: Define the sigmoid function (use pow)\n", + "def sigmoid(x):\n", + " ...\n", + "\n", + "assert sigmoid(1) > 0.73 and sigmoid(1) < 0.74, \"Sigmoid is not correct\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : Define the forward function -> neuron function\n", + "def forward(x):\n", + " ...\n", + "\n", + "#TODO: Test the forward function\n", + "for x, y in train_set:\n", + " y_pred = ...\n", + " print(\"Prediction:\", y_pred, \"Actual:\", y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# graph of the prediction and the actual data\n", + "x = [i[0] for i in train_set]\n", + "y = [i[1] for i in train_set]\n", + "y_pred = [forward(i) for i in x]\n", + "\n", + "plt.plot(x, y, 'ro')\n", + "plt.plot(x, y_pred, 'bo')\n", + "plt.axis([0, 11, -0.5, 1.5])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pretty close to having everything correct on the first try! As you can see, the separation between the two groups isn’t very clear yet. This is where the loss like before comes in to help us improve.\n", + "\n", + "For this case, we’re going to use a different loss function: **Binary Cross-Entropy Loss**. This loss function is specifically designed for binary classification tasks *(predicting values between 0 and 1)*, which aligns perfectly with the output of our sigmoid function. Pretty neat, right?\n", + "\n", + "Here’s a breakdown of how it works:\n", + "\n", + "---\n", + "\n", + "#### *Binary Cross-Entropy Loss Formula*\n", + "\n", + "The Binary Cross-Entropy Loss (Single prediction) is defined as:\n", + "\n", + "$$\n", + "\\text{Loss} = - \\left[ y \\cdot \\log(\\hat{y}) + (1 - y) \\cdot \\log(1 - \\hat{y}) \\right]\n", + "$$\n", + "\n", + "\n", + "Where:\n", + "•\t $ y $ is the true label (0 or 1) for sample .\n", + "\n", + "•\t $ \\hat{y} $ is the predicted probability for sample  (the output of the function).\n", + "\n", + "•\t$\\log$ is the natural logarithm.\n", + "\n", + "\n", + "The overall goal of this loss function is to minimize the difference between the true labels  and the predicted probabilities , guiding the model to make better predictions.\n", + "\n", + "Next, implement this formula in your code to calculate the loss for your predictions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from math import log\n", + "\n", + "eps = 1e-15\n", + "\n", + "def binary_cross_entropy_loss(y, y_pred):\n", + " # We clamp the prediction value to avoid log(0)\n", + " y_pred_clamped = max(min(y_pred, 1 - eps), eps)\n", + " ...\n", + "\n", + "#TODO: Test the binary cross entropy loss function\n", + "for x, y in train_set:\n", + " ...\n", + " error = ...\n", + " print(\"Error:\", error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "as you can see the error for the data that are one is so small, but the first one is the one we need to update, the derivative are more simple here !\n", + "\n", + "The derivative for `w`\n", + "$$\n", + "\\frac{\\partial L}{\\partial w} = (y_{\\text{pred}} - y) \\, x.\n", + "\n", + "$$\n", + "\n", + "The derivative for `b`\n", + "$$\n", + "\\frac{\\partial L}{\\partial b} = (y_{\\text{pred}} - y)\n", + "\n", + "$$\n", + "\n", + "*Bonus : To better understand and apply the derivative of the function BCE with the logistic regression, try calculating it manually and never forget the chain rule ! :)* " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO setup the derivative of the loss function \n", + "def derivative_w(x, y, y_pred):\n", + " ...\n", + "\n", + "def derivative_b(y, y_pred):\n", + " ...\n", + "\n", + "def derivative(x, y, y_pred):\n", + " return (derivative_w(x, y, y_pred), derivative_b(y, y_pred))\n", + "\n", + "for x, y in train_set:\n", + " ...\n", + " derivative_weight, derivative_bias = ...\n", + "\n", + " print(f\"x: {x}, y: {y}, y_pred: {y_pred:.3f}, Derivative Weight: {derivative_weight:.3f}, Derivative Bias: {derivative_bias:.3f}\")\n", + " # Plot the derivatives\n", + " dw_values = [derivative_w(x, y, forward(x)) for x, y in train_set]\n", + " db_values = [derivative_b(y, forward(x)) for x, y in train_set]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Plot derivative of w\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(range(len(dw_values)), dw_values, marker='o', color='blue', label=\"Derivative of w\")\n", + "plt.title(\"Derivative of Loss with respect to w\")\n", + "plt.xlabel(\"Index of point in dataset\")\n", + "plt.ylabel(\"Derivative (dL/dw)\")\n", + "plt.axhline(0, color='grey', linestyle='--', linewidth=0.8)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "# Plot derivative of b\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(range(len(db_values)), db_values, marker='o', color='orange', label=\"Derivative of b\")\n", + "plt.title(\"Derivative of Loss with respect to b\")\n", + "plt.xlabel(\"Index of point in dataset\")\n", + "plt.ylabel(\"Derivative (dL/db)\")\n", + "plt.axhline(0, color='grey', linestyle='--', linewidth=0.8)\n", + "plt.legend()\n", + "plt.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# OPTIONNAL BUT RECOMMENDED : if you want reload the weight randomly at the first value \n", + "\n", + "w = random.uniform(mini, maxi)\n", + "b = random.uniform(mini, maxi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now try your best to implement thetrain version ! *The same way you did for the linear regression remember it !*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Train the model\n", + "\n", + "epochs = ...\n", + "learning_rate = ...\n", + "\n", + "\n", + "for ... :\n", + " for ... :\n", + " # ~ Also 5 lines to code \n", + " print(f\"Epoch: {epoch}, Loss: {loss:.5f}\")\n", + "\n", + "print(\"Final Weight:\", w)\n", + "print(\"Final Bias:\", b)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x = 0\n", + "print (\"Prediction \", forward(x))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for x, y in train_set:\n", + " y_pred = forward(x)\n", + " print (x, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Plot the actual data points\n", + "plt.scatter(cluster_0, [0] * len(cluster_0), color='red', label='Cluster 0')\n", + "plt.scatter(cluster_1, [1] * len(cluster_1), color='blue', label='Cluster 1')\n", + "\n", + "# Plot the predicted probabilities\n", + "x_values = range(0, 11)\n", + "y_pred_values = [forward(x) for x in x_values]\n", + "plt.plot(x_values, y_pred_values, color='green', linestyle='-', linewidth=2, marker='o', label='Predicted Probability')\n", + "\n", + "# Add a horizontal line at 0.5\n", + "plt.axhline(y=0.5, color='grey', linestyle='--', linewidth=0.8, label='Threshold 0.5')\n", + "\n", + "# Add labels and title\n", + "plt.xlabel('Data Points')\n", + "plt.ylabel('Probability')\n", + "plt.title('Logistic Regression Predictions')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "Congratulations on building your second machine learning algorithm !! now let's level up the difficulty and introduce you to the concept of neural network, good luck ! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day03/2 - Neural Networks/images/One_Hot_encoding .png b/AI/Day03/2 - Neural Networks/images/One_Hot_encoding .png new file mode 100644 index 0000000..b4ec1a8 Binary files /dev/null and b/AI/Day03/2 - Neural Networks/images/One_Hot_encoding .png differ diff --git a/AI/Day03/2 - Neural Networks/images/backpropagation.png b/AI/Day03/2 - Neural Networks/images/backpropagation.png new file mode 100644 index 0000000..c0677b6 Binary files /dev/null and b/AI/Day03/2 - Neural Networks/images/backpropagation.png differ diff --git a/AI/Day03/2 - Neural Networks/images/matrix_multiplication.png b/AI/Day03/2 - Neural Networks/images/matrix_multiplication.png new file mode 100644 index 0000000..fdd1f20 Binary files /dev/null and b/AI/Day03/2 - Neural Networks/images/matrix_multiplication.png differ diff --git a/AI/Day03/2 - Neural Networks/images/y_prediction.png b/AI/Day03/2 - Neural Networks/images/y_prediction.png new file mode 100644 index 0000000..344afe4 Binary files /dev/null and b/AI/Day03/2 - Neural Networks/images/y_prediction.png differ diff --git a/AI/Day03/2 - Neural Networks/neural_networks.ipynb b/AI/Day03/2 - Neural Networks/neural_networks.ipynb new file mode 100644 index 0000000..39c7df4 --- /dev/null +++ b/AI/Day03/2 - Neural Networks/neural_networks.ipynb @@ -0,0 +1,678 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "# ~ PoC AI Pool 2025 ~\n", + "- ## Day 2: Neural Networks from Scratch\n", + " - ### Module 2: Neural Networks\n", + "-----------\n", + "\n", + "During this notebook you will see concept that you already see in the previous notebook ! They are just here to remind you and to show how are they apply to neural networks... good luck !" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#import the libraries \n", + "\n", + "import numpy as np\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "attachments": { + "matrix_multiplication.png": { + "image/png": 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R/+0MATjplxvdsVfPCgU4hspKB3nZqzHylQcbBaCywkFeHmwkCNx4QwGOtbLZYXYbCRyQZ8DDcWnSwpgYQsE31sF6nwNq8k0OAAqUHpwfs4rQhzp8qw6AsQXrEIIajpUy6aqY+dCT3l34JNbIOuDhWJk0FUZTGDs7xebGyBr1+IYGIAWUyZGQY16clTIgvTmCcsaCGo7x3BhomzBi0vpcF5YGrNKTDThmtUN8YzwFeArwFBicFODheHDOOz9qngI8BYKOAjwcB92U8B3iKcBTYHBSgIfjwTnv/Kh5CvAUCDoK8HAcdFPCd4inAE+BwUkBHo4H57zzo+YpwFMg6CjAw3HQTQnfIZ4CPAUGJwV4OB6c886PmqcAT4GgowAPx0E3JXyHeArwFBicFODheHDOOz9qngI8BYKOAjwcB92U8B3iKcBTYHBSgIfjwTnv/Kh5CvAUCDoK8HAcdFPCd4inAE+BwUkBHo4H57zzo+YpwFMg6CjAw3HQTQnfIZ4CPAUGJwV4OB6c886PmqcAT4GgowAPx0E3JXyHeArwFBicFODheHDOOz9qngI8BYKOAjwcB92U8B3iKcBTYHBSgIfjwTnv/Kh5CvAUCDoK8HAcdFPCd4inAE+BwUkBHo4H57yHzKjNGG4wYWo91qUxK3tM1e2mkiZjXr2hosVY32VqU5l7dZjOiJnMcERYyAyL7yhPARcU4OHYBVH4on6nQIfKLK/S77qi3pKhYv53okhb22kymnCEzv0+Cr4DPAW8ogAPx16Ri6/MFQXMhGTb7hMKO+P16RLtjVaj1oAZTLzEzNWU8e2yTgEejlknKd+gdxQwY7jGgF2t8VoWdkZh55LUEq2yx6znQdm7OeFr9w8FeDjuH7rzT0UU0BmxvHrDtsteaCScMddjSWqJtqXXrDfykjLPd0FNAR6Og3p6BnDnjCa8pMm4Q+6ddtgj8tJUSC3RtvXxoDyAeSrkh8bDcchPYcgNQG/CKlqMu73cpqPBWa8unS7RqvS8mBxyXDMoOszD8aCY5uAZpNGMX6rUewWgrFfec1XdoSYs44KHLnxPeArgOA/HPBcElAInCrWsw6tvDVa3m3i7i4DOPf8wTxTg4dgThfjrLFFAZ8QSFYHTFDPB6Gs39UZeSmZpfvlm/KcAD8f+05BvwQMFDCasU23e6o1DBxMwZaWOrFznoff8ZZ4CgaIAD8eBovRgfY7JjLf0mlmBTo4aOVmkHayTw487uCjAw3FwzcfA643ehHEEoyw2e6VGP/Aoz48o5CjAw3HITVmIdXh/toZF3OSuqZp2E3LUDjH68t0dQBTg4XgATWbwDeVihY47AGW9Zd76Lfg4aHD1iIfjwTXfARsthuG5dQbWEZPTBndmqTUG3kMkYDzCP8iRAjwcO1KEP/efAiYzfqPVyCl0ctT4iUItr7LwnwH4FnyjAA/HvtGNv4uOAj3aoDaloIfyixU6HpHpZpe/xhkFeDjmjLSDtWEMw8+Vh5LK2BmdeSXyYGXefh43D8f9PAED7/HNPSEsGiNoPl3CWyIPPMYMgRHxcBwCkxRaXQyeqBTOYi/zksZuU2iRne/tAKAAD8cDYBKDZQgYhle1heQOnjNMJ+dpeBuLYGGsQdMPHo4HzVRzP1CTGT8QIk4fzvjrXFLZauSeZvwTeArYKMDDsY0W/JGfFChqGiCiMYLmvdfUvImFnyzB3+4VBXg49opcfGW3FDCZ8eNBE8vYWdT1raS1j4+/6XbG+QusU4CHY9ZJOkgb1BlDIFSQt6B87SYfWmiQ8nO/DJuH434h+wB8aHX7gNJUIOA+kKPh9RUDkFmDdUg8HAfrzIRUv4xm/FxZaLt+uBOc+TynIcWJod1ZHo5De/6Cp/e7+ikztDsYZau8TMnbVwQPlw3wnvBwPMAnODDD61CHvCeeO/hOKdby+fQCw0X8U3g45nnAXwpgOJ5dG2KxNN2Br8tyfwnE389TgBkFeDhmRie+lnsKGEz4pUrWFceyVwEAz8avZpLwNE326r3g1c0ql2Dqf6HeyDvouZ9+/gp7FODhmD1aDtaW9EYstUTrP+rZt8AYjk+mjH1oKAAcwnGPlrc+HqzMHdhx83AcWHoPxKfpjVhSHusJ8RjA8YUqybzP/jEEoB930nFTDx9OaCAybvCNiYfj4JuTUOuR0Yzvuaq2l2391xt4hmNJJIHDd7w7ZdZHnErHFS28cUWoMWVo9peH49CctyDrNdtYrNqSYYHjJbvixzxxz60AgDsee2JGyuoLNqCXzHh+zDwZLNm8iFM4zq41BBm9+e4MTArwcDww5zWQo9Jz4h5NwPGQobcOefqJLxK/XJc0ZcLTtwJw6+QUqfPmHsdwfKlSx9u6BZKjBu2zeDgetFPv78B7NKZVx5WvvDCqoKqVK+kYPD9lHykON04bCwBYJAk4HKeWaN/bWLP4UKO/JOPv5ylASwEejmnJw190RYHSBu1nO+t+/nbOMHGkVAjWhN3ybeIFthGZkI6fil9OAd/lC14B4JXIZBKgrQccS8cPzi0DYgUQK1YdV7qiB1/GU4AdCvBwzA4dB0kraYU9b3x/A2HTLe9tnfzEb6VCEDf5ebax2KY7ptodr17cP3B8NFd1y6RsNOqv99QPkrnmhxl4CvBwHHiah+QTd5xvGzXfIiQCsUK8PvveOY9IhUAqBK0FuVsvWwVVijDrH0a7sKzoLzjOqTN8e1SJ4BiIFZOl1SE5hXyng54CPBwH/RT1awdbe4zLkprumF6AwEj4Qd43+xpq2/Rv7vg44n9DpEKQ8s6rRjO+91ogDN36C45RlibRF0VArPi/iVBMfn55RZfaN2NkZcJ4EJ1lm1RlYgSIktvO+aNBTAEejgfx5NMOPa9GPW3bzZ8QOlMgVjwwp2TjmVYTEf03Om3drbP/vvrPt0iFoFaWqjdiR/ID4QbSX3DcTLiB7M3oAGLFb97L/dM0+HJ6UFJyvUlLS0Lni/JowlSahGN5FHHOw7EzqQZlCQ/Hg3LaaQd9Mqf75e8qyW/zsSsrjim6yDtOFJ8VSETPvXybVAgOvxCO47jeiKWVBiJmRX/Bca/WErPif9HXgVjx/qaah+aWALHi9mkFGWV9JGXsDyhScH1CBIhIqFcmjI+W4xCRERwrEyOis3CIyDwc29Nu0J7xcDxop97FwDefbR0pgUCD/j7cejO7Wk2tV9fVOGxZuEAiWvevoVIhKD+wG8dxoxm/fEPvn6bYWfUcRLpjg8kCx5fK+hBlsir6XlhRgXQXh690UklEHtu0EFnRFMC1wTGqycMxSTH+ICjgGCMkLIMJM2O4yYyr9Vi7ylzbYSpqNBY1Gms7TO0qs1qPmcw4huEGE6a3Lg9+/lihQEOHYeGBxts+zEdYc/u0gqWHG5u7XbiivbY9UiARTf9stFQI9jw8nHx6UeOADbC5M8vuhTR1Uw0QK95ccwPH8Xdj4TEQKzamtZKksB3UJ0SMT1DiUP4ltRM4RTpGNXk4tlFs0B/1JxybzBB8e7TmvHrDiULt/mxNvNzDjlC8XL0/W3OiUFvUaOjTQfjmf/5Q4GqlCuELgpVHvimNS29z1+Di1O8FEtHfoh/f/2K4VAjyYr8na6r0AzBvKZL3z5XrSOn4RwiuadX/5C24m3cytxvH8dl76xHpXDmJKBPGRyTUy6NBNGWrjpeOSa7hDxwp0A9wbDLjZgxv7jFdqtQlKjzgL/0ncKJCfaVa36YyYxhsk/8xp0DS1c7nlsPPbfT3esyN0/k9NLcfKTwtkIgEElHSnhipEGy961ajVkPWN2P4/mzWd/OcNRj9UFLd7mhBsfhQIxArHptfioYfc6IZ0XB6XC1JEHSgTIyIGO9gOMHDsQOR+FMbBQIKxyYz3q0xp5Zo4zJZXldxmaoL13W9OgzjQdk2uS6O9EZs/emWf84qRgjys0nZn8TXFtXZgNXFPThe3VF3x5JHBBLRirMbTk99UyoE8qUSak2TmYsI9CwzCf2r3d1VZ82YGcP/OrMQiBWbrDqK3ZfaET3f+P6G0U6T5gi+vLKCyjb8sQMFAgTHJjOuMWCZVaxv+Diu2Oxag47P3eAwycRpVYtuTkL9b6fmIuC465PCb48qO1WOop+rW/GXt70rkIjEu6a3lxYh14/eupsONZU9JneIFrrlRws0Jleh57entwGx4k8f52sNlstnCnp+NzUPiBVPLipv7HShdncgF3/KU8CZApzDMUbszhU0GHZ40guztWh3yNVlzfYyivO4B1PJpbK+tzdUIxQm8KJsz6V25gSYn/KdQCK6Z8VTLX3tF2bPkAqB7NOpzrebMdyj6p+tKQ5YO/n1boH1ycXlQKyYt6+BJEXeTTVyFRF9UZRbY7cBSNbhD3gK0FCAWzg2mvGqNmO/aBX3Z2tqO5DXAs3wB/ilfZkdTy+FqIH+xD9UpRf3ejXmQ/knkco4rfyiurUZicYtuQrnRkxmnIMUTY5fPwEDYvSgLo0r2ZgYfFphD6JqBcUZpKnT8B8Cpn87NTeVVhfvTEC+hKcAt3B8oYJ17wDv1mfGDf0gnONerWn1iebhn0GnXiBWCKbkfrW73nsXMryitTps0UiBRLQqfROO41e/WywVgpMTX3ZH0pp2Y4DhktPHHcn3oFKftB5+c7yzwS6EhcmMvbHGEmVp10UvvkLcUZUvHzwU4AqOjWb8aAHr6Sy9w2K0Vo8WaA2MFKQDYdLLGrRf7Kz7xTs5CIjv+aJozclmtc6tiEc/5hc2vy2QiN7e8wmshmHb74GeeDfTUtzdhWH48cKgmHRWYLqu0wPflDdqEZ3PFTkapcyIq0WXVh9vdkcuvpyngAMFOIHjXq15Z5ZfFmysLCeykZ1Z6k61j5DkQK+gPT1b2POmVSgDYsXoqOsH5K69xRgOQXJihUAiuve70Z1q6CFduD1WKgQHxzxKf3tT9wDZ0Esp0jIxnZyb2ADEiqeWlDuTZQlhDwfEitl7+ZiczuThS1xQgH04ru0M0gV5o9U4IM3gdl5oD19gC305RVqdWe4ukIILDnBZlJhzFKmM0yszUYXEJ++TCkFZ4g6X9cnCAaNBbull9P5W681DP4LejPHnXbjPbEprRTLylFg7hQZJLv6ApwCVAizDcXZtUDvLZtcamIg8VAIF7XF7r3F5ctOdMyyhL4dE5s1LbLjZxoKuvLS54g/z/yWQiNZe2IaGX3nskFQIdo28iwk12lVm8rskRA/OlnmRHC/2DMTcYZ8UuiRO0tXOW4iYnC+sqOj2MSany4b5wgFIATbhuLk3SOViKig0dpvYkZGzogERkcDKFPJoGLXLesbl//k3NdPjan9KuOoCseJfs0ukZ1qM7L1n/rtRLJCI3k38ghzEkddgkIrc9d+RJTQHRjOefr2ft3CpM+7DMY1BhcuBP/JNKRArlh52nUzvcnnf7URMzpGSkgqlzmULfCFPARzHWYPjEIpa0Kfz13UPBusCwAbHMIIiANzDcUpu9yurbKEvX/y24igl9CUrDD3raJRAInpw9XO9OovGozHzglQINt/5a4OKqQ6kVxvCISwuVuqMHvbwHCl9IqcbRXerdfN1UqHUoVB5f55WcNlvVZLj4/nzgUIBduDYxEk+CF/sKJiIQnuvqV26WtnPKSnt2gLXyqNARKISz4qOSFRCRLZIx/Lo8QlKS0xb+zbYO9tyrvWhuVAEQ38fbLmpqFKx17ylpd2Kw0hlfKnqKtl46gdiqRBcXvQ1WeLxQG/CKltD0ujtRCGjHTxnCkQQKQQjN9c4X0IlPRrT80RMzlsmZSdd9WuX1d0j+PJQpwA7cHyuLMQ+Ts+Vef5mtIAvLo+Oio6GAcJtuIzjOAWOCR7gBo4bOw2LDjaizSLklbv4UKOyy62rmD/sWNBY+rt5IwQS0YYM235d5/VS5PrRU1PlVeMaA5ZbF9QbCc5v7p1Zao3Bxy+n/Jsa9KakF36nxFrcI8l4F15Rla88sCngLxwbTIGIROG8cvwvyazSUwMnOiVESkkAACAASURBVE+zJXw4lIXlCVEJShip1hYpkWs4vnZDFbnZEk73x3COD88r3SZzsXfv3G2fS56WvimQiCL320nBF+d+KhWCszOm+NCs0Yxf7G8/IK/4pEPFyJrCHSm+2FUHxIrnll93VwGVf22NybnkkGtdM/3t/NUBTAG/4BjDoA+0VxwfVJWvtxiNNAuQCB8uT4xOqMflUREJidScDhxKx8nXutBXLZK2Xl1deSoPhtbl9PdZ8iKBRPTw92M1Blv+N21He+xtMCFes4KSa9PLfvSLi7wPbFbbaReKzctRwuqdKtPviChCiZc76G9ffdwSk3PGdseYnPQ38lcHNgX8gmOTObSj3O7P1tDBMRSHIyJgfjMc6ovHR0DFsfXHunRsMGEbUlvu+9oS+vKWidkzt9cW1nrw07V2x6//46/uRyrjrJocakOKmGVSITg+4QVqobfHBlMIbOtdqdEz2E7wPPQ1JyHO/uPLIo9Vd120xOR8c82NwR5axSOxBk0F3+HYjOEFDSGmHHQWmvIbDDTrECbOQft1TqphFuG4ukUnSWxAghUQK/46s3DFkaaOPmNgmDCnvghh8aZMmPiO+ou/909SIag+fZxa6MNxS29QWyKfLNKyY/tIkOZfs2GywZXHbG9udxRLze/57Xsw3ul/Fpc38TE53ZFpMJX7DscaAxawmJnOMMpWyQ65Wuvr7o3/fJJR1vcOJfTl4wvLAhx0xoyZn1j3mkAimnZwrsNwindulgrBgdH/dij37VStx4LKb57kn4xKHYtYjOP4oSudQKz49ZScFlfJBh2ol1uj/vvnMNLTPV8U5d3kY3I6kGfQnfoIxyYzHoBY8uSa4fTgSo2ePRcKpgy0P7PjWUroywlrq2RF3oW+ZPok2nozDn8jkIhG/fCKweQojO9/+gGpEJTsiaNtwLuLJ4uCK8BQBZveMzZSvLQS2obPZKYXbuw0PLkI+rj/bmremQLHUES2RvmjQUABH+G4WxPUn5/ewnev1kfzJm85pE9rjjnRjAQiIFb8anLOl7vqyhttu2feNuhP/a3yvUhNoagrcGin6mSyVAh23n+nQ7mfpwYTfqWa84wwTGZ/9xV1u392FDSkuFqpQtuwDG3DjWYMmS0DscKrzAA0feAvhSIFfIHjARMmhly356/ruBaQyxu1X+6q+9VkS+hL0edF359oVvka+tJ/Vrtam4eweFtWonNrx94YIxWC7LUrnC/5WaI1YOXN/WyNc7xAy7WGahoRYHPcqkrm5JpujckZc4KPycmcbAOqpi9wbMbwbZe5cpkjITKQB3GZKu7g+FxR7/i1VUhcAmLFs0vL98s9GEJxzWI6o/6xtS8LJKJPkhY4P6vpymWpEGy6/ee6bhhak/WfxoA195pP9Edk5B1Z6vz6QGRTbOoyoKjTyVe9oCHKUQ3Eijl8TE7W2S4UGvQFjpU9A0pTgXC/tY/GAtnHmdx1sf3xhbbQl+9IqzOCI17BRwfmCCSi/6x/3eXA0j6eJBWCjPm2EEIuq/lZqNFjNe2mgzmagL13c2r1ehOmc1SS+zkOt7evOKIEYsWDc0rc1nB1YaM1Jue7G936W7u6jy8bCBTwGo5NGH4+pFytGK52eTULoSkRR3T0GVccaUK534FY8fv38+YmNtS0enbLDgxDxV7eJZCIfjP3nryGYucndt24jryiu25UOF9lvURrwEqajFwbXaRf1+mNGNcKCmfioEymP5xqcb5EU3L4Suf/ETE5X/y2okfjZTQjmnb5S0FPAa/h2IzhCdeCKNMHQ7T1WC1RofZfX1FQq5mxvRatJSBW3Pd18YbUFnpX7ABzSGa1AqmMd1476PLRGfO/kApB2sdvu7zKRaHJjOuM2NUa/Z6rLPPVtsuq1BKtzoj5HImC2XipwUzgseUXJd+bAX09hkTmeRvpOKPMEpPzobmllXxMTmbTMABqeQ3HIR07kR6U/bGvOJXX/epqW+jL51dUHLnmhdIwMJyk0qsfinlBIBF9cWSJyyfqurs23f5zqRA0XbnssgJ3hUYzpjViXRpzcZMhOc8vDcbuq+qMG7qmbpNKj+mMXNvMyKMJ9I0m3MgtcU4gmWB5RKLyv8uuA7Hiq91eR8K+3qR9cA70KLljeoH/6V24mzi+ZRYp4B0cYzieVx/ynnjuQLmwwZdIaVtlbf+eZwt9Gbm55toN9kNfsjLl7++bJZCIno0d76617LUrpEJw7I0x7ioEptxoxg0mrK7TlFaqO5Sj2eUp72LcZVWiQn20QJNfb+jRYkYz7v+HjouRQs9MSwwpq0+mMgH60EPkRXBMvQtFBLxY2ot2cYvqvPZ371abnl9eAcSKn03KTg6+tzt1sPwxKxTwDo71RqxfNsTdASi75ScKtXrGYWSaugyLDzWGfQzzpAGxYuhH+YsONjYGsavruovbBRLR77+5t0jpIs8mYqad998pFYKqk8ms8BYrjZgxHCP+dEasW2Nu7DaVNxvz6w017aa2PrNajyEfd1SNlSe6b8SmlJBHUfHXJRyT8bLxqZtgZL7xa72LUEp2Y7LUEpNz89lWspA/GJAU8A6ODSasXwN0FUc+P3Tk4ip2UZhsbX+2homeV3FD9cGWm6Th2r/nlm49F+zr5MKNLKQy3pvtFmpL9sRJhWD/0w8MSEZnZVDKRBRGyi7UKtJL2EvHNuDGcbymVfcTcTYQK1JyfYzM9/WeesRvSw83sTIQvpHgpIB3cGzG8Hg5y/stJBp6OEjLmTL2LgAAd3AcL1fThy84cq3rBSKhA1obr6yq9HmBBZIbujU996/6n0Aimn18Gc1zD4x+SCoExTu30NQZ7JeImKvKLLtQq05wbJOLSXItOtgIxIrH5peRJd4erDoOzeaY+1572z5fPxgo4B0cm8y4B9DM4MI9pHH56hUj77DsV3MHx1syVC6juxlNmDS15V/W0Jc/fSt7elxtwU2vVYH9Nd9TEj4XSERjNk2k6UD16WNSIdhx7+00dfhLREaYiIjxVE2FZdfOKh07CM4WmpnM2F9mFv6o1Nrkh8KBGpOTE+U4P8H9TQHv4Fit74fYtasXvwKReMjTY+esGMmldLwlQ6XW223E32zVz0tsGPJ+HhJM/jKjYHlyU3tvoBwJ2GCO789vEUhEty14oLzlBk17xye8IBUCRQyd+Exz+yC6lAX37WAIbNvPpjuGEVkpP2qA7Lj0NiBW3D6tQOdHBMHT+d23EjE5n1pSzlGOLtuw+KOAU8A7OG5XceCPty/2PgBufT5xNSlZo5KJKVKiZHXUu4/OSFp+WrUlOZ5rOCbDylwu7yO3UIBYEb6gbOeFdl9mJyvamuEUxrC3LtWIBK8Nn3x5+LmKDKQy3p9LF7NYqciSCkHs0Fu0HT6N0ZeuDcZ7UOS2b/Y1+DP43Gr1cBST88ui/ND5RPNnyIPnXu/guLbDxIWyYvmCVwAY+mgUsUcny3z1XgDuXSRJc9J7cA/HtR2mA/LO0VHQVhT9jV9741yRj2EPoTkUsAawJ3KLIBQmyh0kLPZZrl3V+c+Vzwokonknv6Vv/eyMKVIhuDT3U/pq/FU/KZBW0IOYqsI/z46GDsMTREzO37+fl8bH5PRzVoLpdu/guKiRo1hc0GQCDPno05PtksjHAHjs1W3tLnCfezjefbkbLZhfTM75YlddmefQl+S+jW0z3ZKCGuY8VVoNVO3nnGLBan+BzbOJu2cKJKIXt0ymb7S7pgp5RXdeL6WvyV/1nwIT18NgUu9Iq/1symjCXo+5gXh1bwb/TeMnOYPl9iCBY9WWQ/GPDgG3PvT03QDcN6fYBRZnBEJZ8XurmvgX7+R8vrOutMFzJGIL+OLy6Kjo6Cg5sdtj2+pxCcewENbk8PedLFYgEd2++KEbbTfpH3N50SypEKR+IKavxl9lhQJljVqEoaxkG5i2zWJw+f1JPiYnK/PTz414B8ccKSsQ+Fq27J6KXU4qkR0OuJeOp8XB3OzUvzfW3EgrpFNWWLAVysLyhKgEJVRK2BQRLuCYqk3mZvZTyy4glXFSQQr9Ewx9vZvv/JVUCBozL9LX5K+yRQFJAjQifnqJW2ccrx6ETOiAWCEJzHaEV53jK3tJAe/gmJOtPAvmNn762lBip+uxN+OdtMaoDvdw3NJrErwLs0kCsYJqYjxqQdkOd1t5hC2qPDE6oR6XR0UkJNoZpTrAcQDkYmVvq2j5fwQS0cJTqz0yQ87676RCcOS10R5r8hXYooBaZx76EXTmdMtRXj4p9kwr4tj3+JicXpIu2Kp7B8fcGbqtXfnRrWDoo/MSiX28FQsc5OJAwbFajy08AC32ifTsxaWN2nn7GoZEWgzd7pxREJ3c1NrjYOgG1ccRMHYBtJ2IGI8ctywTTYVjq08XtzwwfufHAonolbipTB6za+RdUiG4cewQk8p8HbYoEHumBYgVwz4tZKvBQ1c6f/oWdPwb+21Fn5aPyckWXQPdjndwzJUbSHIiVBwTtm7SbYvuBuDujzKRlZudEpl76dhkxpu6DKSyYtJ6uOViMmOxZ1ruJxK2A7HiJ29lT9t2M6/GlvcXWpuOT4CZ3OEenZ0Rmw2O4SXqz6bQYHHOo9PWCSSivyx99GanZ0u6ssQdUiFIfPI+FjvAN8WQAg8TYadYdHq+VNb3JyKCyr/nlt5oDpbg2gypwVdDFPAOjrlxkkZmFROmJSMdhdW4YrOTcQXHcBwvt4Q8/pjYIUHiBjVx2VFF14vfwghb6G/cd5Unc3yMQsAF/50sOYdUxseKzjBp/+CYR6VCULg9lkllvg67FDie3QXEilsmZte1s5b3oLxR+wARk/POGQXy633sdphvLQAU8A6OuQghtHzxBKimQEbHSCmRJhv7d1emxxzDMRlCqOCmhsRcIFakF/dSZyK7Sv0hJYTQQ5KSYAi1Vd/VNGxZuEAiWnpmLbW37o5vpp2UCsH2f9yG43aOiO7q8+WsUwBlj/5giwfTF6+e26UyPbccWs3/v7dzgjDitldjGYSVvYPjgR9g0xqtHC0V9EU5/LMi52wOyi7DkkON6PMQiBW3fZi/8EBjQ4cvEZNZYbvXt0cKJKKI+A8YtnZi4ktSIbj6nes49AwbCUA1dwE2q9uMLb3mPh1mJDSlRjOuN9EHgApAZ717RP5NNXrrX2Y7g+I71picW4I+1qB3JLPWNmOWkNYGE96ng5lwK1uN2bWGihZjU4+pR2vWEwsZcoXREoLVemtQ/+8dHA/s8PN59bZoAmcLoQPV76bmPUHkHp3gPljtNlkbQm20tN7fVHO1MtDh55ekrhFIRHdHP97QDTXYHn8tudeg68cffqJp9S6Nm8eWWalAhJ/HfQg/f6VG36U2m80447DVrPTX90Y+3wkNK59bzn5awlm7LTE5o5IGSExOE5FVoLXPnFmlT8rTMEnlteeqOilPc6lS19JrNmMwp4HvUxWQO72DYxzHB3Byph6tXTLpJxeVA7FibmLDrybnALFi5VE6pDud1/3aaouXFFpgSVc7AzKD+JGiVKQyTimVMXyi7NOpUiG4MHsGw/oBqIYy5vVozCVsJGe6WKFrV5lNMKtIAPru+yM6+oy/nQoNK/dldvjeips7vztmicn5SXytmyohUKwzYmo9Vt1mTCnW2m3suzS+cl+47bIqrUzX2A0hGcnOQTh4r+HYjOGJin4Keeye1v7ME7o34Zpj6tKEyx1ArBB9UYQSUP4YSOiMp/gAhbWamdtrbyHSAAOx4t5ZxetPt3A69zUddXcufUQgEa04u4Ehe/XW3URe0e2lRQxv4bQaAuJrNzlJXXq+AuaQDmax6PuTzUCsGPGVi6ze/pN95wWYOxXlIgkxVQ6OawzY9RbjgRy/Eie6RIaTRdoujUWh4T+RWWzBazg2YfilSp3LQYZ04fkKnXOwY5SYPeFyx1e74UflX2cWtjGIrtnZZ/z2qPKuT2B8W6TxkCTUV7VwYns0btt7Aolowq5pzHlCvlQiFYLTU99kfgt3NbUGrFRp3OkpG56frHXtpsFggjn0gvOHQml/d4zu88vnnp/K6/4N4dn09JLy5u5+29vwqv8aA1bfZTqcyz4QUxkprVTXq8M4lZa8GjWO417DMY7jLb0chNnkUvKlzoG746ZuF5+1a1Og5PLkIpjE4ZmlUHfxegxd1GAH6u++1I4ibyFcfntD9aUyNs2PFpxaJZCI7lnxVEtfm8Oj3Z0aNZotd90qFYL6S0w1G+6a8rNcY8BudpgOciD7uJviwgYIykEoJB7K6gRixa+n5LY4ehj5SWPL7TnV6uGfFSHPpoLaoE6boNFj7Sqzn3oJdwzgsjyjUtenw4JEqeULHJsxPC7TjR9zf6OqS4p7LNx2WeUyvYLeiKGgQmcLeyqatCjy9zIv90Zkxb0TfoBxvNDfM0vLWVEUHso/iVTGZ8q9CDeRFxsjFYLkcU+xs9B9akWjx1p6zSeL/FIFepxTlxV2XVGXNEFQ9qnjHN40diW0Z5/JmZK3vkP/OLEpPeT9vLO0MVg4HCRt00YzptJj56/3z5e3vFqvsW3k03aUy4u+wLHJjKeW9MNacrnAWClMLdE6ayoQ2b/Z10AKxfvlUJvsWw7K603ar3bX/XqKJSDG8M+LVp9o7vXVn7WyrfpPix4SSESrZBu9Yo89D/9NKgQVyfu9uovFyio9VthgYGXWfG4kpVgbVJ+oOI5fqVQh1squsnl7skh2HMcNRuw1a0zOhAz2dw796a3GgDV0mZgYS/g86R5vTFSoO9T9rM/yBY5xHO/WDCh9RbfG7TTUt+vROkHJ8eYQ4bj+PL3At9Q4Kp35+5PNSCUNxIpfMo2q7MjqL2x5RyARTdrzieMF2vPyA7ulQpAQPoK2FocXdcZ+E38cVuPeq+oebXDJyCha5iurKjmcABxHHqdArFgTNDE51Xosv76f39Ake9R2mPpxj8FHODaZ8cwqPTmGkD7IrNK7E43RwviA8MH7eJvFe2pMNPR6GvedX8vmgLzDPudIFfOcI3NPrBBIRPeuHN2h7vJq6R5+IVwqBAVb13t1F4uVj+RzuznjLR/WtAeR2qKp0/CLd6BJZfI176bV2wlaSKS1Rkac3t7Len29CZOV94+Cwh23ZFXr+0ud5SMc44QZyg55yFu87ZCrPeqMcqot3lNNXXBjurpFJyRivC0+2Ogndzpm5FvoOSPfvtyjSGWcXpHp1dNrZalSIYj7u9BsdIhI51UzPlZW6bDg5BaPL2MfB+zTbcuPNAGx4kFJiU93e3GTNBWGlANixdRNNV7cxkHVpLzgekMjjD5e6DnvBAfE8MmyAvXDjOEF/a0EdPd+Y15e0GBwuYnnQOtXVlUCsWLhAQv+Hr4Kt8J/XDlHFSwIMjWt+rmJDWQiEpp81aXNlX9ccL9AIlpzYatDDz2eprzzqlQIrqxY4LEm6xWC3BTnZFEQqS3+TqQl/eEU596SB7M6f0LE5HxpZYXK3gGKdQZw1+D2ILYIiJdzpcR3Rw0fDd3I5kxmfH92ML7cGMLx/mwNvZqCHGlqPvSZFn6QZ7KC9/z9cItv6Ef5XkfkomYDsUXdjM4wYdLUFmSCCsSKn76VPSOu1iFV8P82viWQiN5N+ILsGMOD1oIc5PqhUvor0TN8IlnNYMIZTkc/VrtSow8SRfKeS9BxQxiZ16NxYXlJUpWVg4ulvWFEIPyH55VyZBdP08/gh4792ZoAc4XvygocxzEMr2rjKJlpIAzpqtqMzK1QwxeU/bhONpy2iS0o2OYLK7wIOADDH9tyS9tlOyWz5x251kVNRPLKqsqUXBjGc9axKIFE9MDqMT1ar42X07/4QCoE57/6iGZ5cHQp+Fcdeg00ujI854gm9M3+dxncnJi1x3PEavp2mFwta9SiQN5/mVGQVRGgWCtGE362LLj0xe5EgbNlLrzDmBDWtzp+wTG0njFhIbqnl1nlncJ+90UotlCdWevb9SjLzjf7GijU9yW3tDX/qa0ZxQ1V5GZLYkogVgyfuwGpjC9VXbFVYnbU11iPROO2onxmd7BTy2TGL1aExqpDq7E3OJQWF0t7kSqsqC4QLhudKhPanf752zmsKN/ouUdvxHJqg8WOwh0KU8vLlF4IbfRj93jVXzhGDzgXIu86ksrnynxxWf7bZ9Dveb/cZrN5TAGDiAOx4vAVW8AgK7Yyyy1t0Ve4Tg7S1GlYfLDxtunHBHNGCCSiIZ9FLzrY2NjpnatrVvQ3UiE4NeV1j9zAYgWdEcutC6VVtyVDtTNLHST2yO9trEGBJlicEfqm3t5QjTh5K5cxOY0mvLI19L6n2/rcGsLSU9Xbq+zAscmM770WMlYWe6+pGaqMHagZcwL6TDvkAF58CObWGxKZV22NSmHJT8o8tzSRZA9Q8k87PPcZ6ZsCieh2yYdowQCxInJzzbUbjD4tTXr9tr/9XioEdefTHJrl7lRvwm6EphbrRKHWujvAHXk8t1zdokNzfSovcOlmUGAWIFZ463fqeTzWGg1dJlIkCqGDuExVny4QamR24BjHcZUeCxX6qnylrFpvRtFYHPKDILuL/y27buE6b3JL226xT7JnZWD88yOLBRLRw9+/qDZoTuV1v7oa2nigvxdWVHjM+JC/+QepECSNfYJsMAAHIR2FNataHwyIvIiwDh61AMZLCdhvpTUm56ccuGtrDCEDEc5QlpSnCUBcC9bgGMfx5t4QePXBgKd+vOeQV954+2j0yi7DHdMLgFgxZy/afmGYW1qZMN6S59SW4dR+5e24egCpjOU12eSVglrNjO21/2cN43nf18UbUlvcGa7vfeweqRBcP7SXvJ3rA6MZT++nyAPOq8i3kmBw2DOZsb/MgEwV4NRfO85bYnJO+KGKRVYxY7jiZogprxyYp6qNc4N9NuEYx/Hs4FbSZ9cysjKm4cKbrRaf6eJ6u22WlNxuJLGi8ECMcktbMk+j9NIudMe5DUUIizdl7nbuUnufcfmRpr/MhCsWiBW/fz9vbmJDTaudTvz64QSpEOx59O/Ot3NX0q4KeQd6WbkuGATkbbI2IFbcPq1AZ80Zxt2sUVtOye0WEDE5n1la3tLNDgapQ+fr2QGFydN9Cs4FZJbhGMfx2s4glZErW9nZIZ26CW6zzNjumGEhOhm6VN36Xu71JhZcesyY+cn1rwkkoo8PzqUuFefjXRfbUbAuhMvvSKszrLnXkl56UioE+ZsYJTN1btmHkgETXqpdFaDdG3oiowCt3+ynmu7Q38HO1ewqNdq4HvFVcaHfBh4mM55VMxBiKkArC3YI7LoV9uGYSOBk5jqgOPnKYnKwM0vN4uq6dgMG3/qJOLvVKTotSnj69NJy18T2pnTm4fkCiWjU2nEGEyM7inNFPePX2sJ4Pht1fc/GZKkQbB32O5POTmT2phde123qDtKXMRM+odZJKQqKPb0zBdD/CIgVlcrATSKa9bp2PbK1F0bmMQ+o4pJjVLoQ1hpTuWLXFR+tAFySxbmQEzjGcdxoxo8WBEUQzqMFWj3bgWJeWgn305YccvRwa+81oiQgX+6qc6Y185KtWQlITXGt1jtL4fJG7Ze76n5JJPf7YMSzUiFY9+5nKl2ABD0Mw48XBsWkU5eQz8cuMxIwn0S2ak5cB9+yk6XVbDXIvB29EXvNunWccNlm38m8BQQFl2+Ekvk5PcMUNRp9s8tiQjSu4Bg9+0J/ewFcqPBKprC5yZG0c7nJdpLQFA/9yAVWplnFmT2X2slGvDq4VpuHsHhbVqJXN5KV+7Tm7zemI9eP37968leTc77cVVfeyIIKhXyEy4Oa9tAzKaVZe0fy7bYHXA45AIVlDVokIMuKewPwOOdHfLTV4o60NqXZ+arHkr6BIhojVtl2mZGBqUeyuKzALRwbzdCLul/cZPdna6rajN6ELpVHE5tq0VkUQiEfjfEJzlnMHvmmFIgVG9NaKbUth8ha6JeTc0rst/ucazqX6E2Gx9aOE0hEnxye73yVecn5WdOkQrDtrXdQTim0niesrZIVcbWkjQMuKcGWDBVNIGzmc+F/TWTPw4oSzLfOLDwAjet9i8lZ1BjaBhXOL+zrzexsbzrPBbdwjOJamMww9lvA4ivukKsLGgwmMwyp4fCzOGggtwsEshBwo+W4MmF8tByHiEyBY8IQLTEauILj+PNw1/tfX7tOACwmsjE9QSTZc+gD/enHByUCiejJ9a/7k9NN3axEonFrPjSPyyjre8fqcwXEiscXlu2+6KPkTtN5o3mgpezakqEqbGCkuKchCyuXVDrzbR/mA7Fi5wX2J45hDzdYY3K+701MTjOGB2cITWeQZV5yvFDrjZzHkMCwGudwjPpiMsM03QGIbpFZBXNeuVXuWMAXVyZGR0dFJ9TjODW+mj0cKxMjIhKV9hXsKIvUxFT3aPJyt9qEIiV6ZU6/8fIugUT0G8k9eQ2uUZ5sn/7gyrcLpUJw8u1x1GrVLTpJQv3vpuYhMeevMwtXHFF29LH2nq8PTYcr+kWYlMc05h+V1FwcowjFd39ayEXjDNs8SKRYBWLFSysr1cw2JLSh7PpBwxscuYQECI7RfJvMMKtTaomW9cyncZmq1BJtt8bsFogtHIfi+xCycFZ0dBZuwVzbVat0THjWQR2FHV7b8e13hAvT6CirM57dRTy92BIIZsd5RmmeM2uykcp4x9UD9i15d4aZTHGiP0iFoPbcaec7DUZsw+mWe2cVI1D+v4nZM7fXFvqdXdgU+q4f7taellvTJucpclvy8DyoH4s63OS2BvcXLpT0orBZj3xTSkYFoHls8YDTVCA+qevkJPxpQOEYTZvJjJsxvLnHdKlSl6jwK9JFokJ9qVLX0ms2Y7gnILbwjDwKRGfJo6PkeH1CRFQC6RdHXLYpK6Bag/pzpa/o1Zh+RdgwXCx1HfHy+5MwxsXPJmU7hC12Zl+1XvPv718USERfHFnsfNWrksJtG6RCcOj5UfR3JV/ren45zF6M/l5dXelPeASTgjUSqQAAIABJREFUGe/fvJPuwNT/8hutrH1A0M+Ix6vHsmG8qlsmZXsdYttj095UKGvQ/mt2CRAr/jKz8AptTE4zhh/MCeF46DTMAwNvOulCvaGi67r9AMdkR0xmiKE9WnNeveFEoXZ/tibeU7aneLl6f7bmRKE2r97Qq8UgCntJFCgOjye0ELgyISqCUByTPbLBMVlEIx3DGMR76oFY8dY6t+6kKFDWo/NLbQ26Onp/3yyBRPSMdLyri96VJTz+T6kQlO3fxeS2qzdU72+GXi3o7+F5pdtkjGR5h8YHWCpb6jo8Vew2y7gDEQJw+jqRCvqDLZa0jQF4ostHdPQZ/0dkjPzFOznHsukS4lApOZCO4+VqLvw2+xOOyZnGcFxvxAwmAl7NuFqPtavMtR2mokZjUaOxtsPUrjKr9VAjbMZghGW9P+51hLEE2q+DrsxRcrIbuL3u2FLuXlmB4/iNZkvkrTI3ZmRqnXnEV1AzMC3O7RJaf2m7QCL6/Tf/LGry13+k8sgBqRDs/vfdlEF5PmzsNCw62HgbkRjixyAJYR/nLznU6FWq7CB3jvcTCDxTMFA18mosaRszr7v+IAtUR+BzJq23xOR09wofMD5BLvmnU82+OX9QwHEgeYj1Z02WQqb8bIdbv4+Msj4ke24950LwvHjjClIZ78lO8r9vR155RioEudLVvjW15VzrQ3Phdyj6+2DLTUWV54xhRjN+qZJ1O3/ZqwCAZ+NXZ7jPC3Oh6ssZ7/7j7qFQq3THYyMnxi857b4yTTueLgVJEGQ0p5/vrANixfPe5KDxjRmY3PXlLtiZH1Uo0ckuNNoVLf1phy7dvOhusEjiaXJdQi2TQi7UxzwcM+E6ujpZFdBn+pZJ2TRWCutOWRL3ZtujW4+294HVYwQS0dfHltE9g9m1hgzo+rHlLwKj2jOG0jSZkts9jkjVilbai99W0CeJ0Bux1BLWnfE8wbEs89V7ARjy9BMz4j9dlzLti3f/MQSAez/78iT7iBwMAd7I+eroM/52ai4QK1CwKrK8vw6+PapEfOIskeT0X/6BtWvmQH7gEo6Lm9jfVODhmAU2Rqnt6IN2v0vkd3hobgnVGnpKwucCiWjMpoksdALHU98fLxWCzCVzWGkt/6Z6elztT4lkw9DCenaJ9EwLmbmV+gi9EePAttQDHK9d/i4AD4yNbbcJMvti7wPgzhmZthKWJCNlDyfb6FQaenWM0iBQ84R5dTvrlZENPhArxJSYnGaMi28mBu/aY7IpE56+1bIPz6F0zEVcbB6OWWBOlKLp9mkFNG3pjRjKEUnuw6y5sFUgEf1xwf1lLZU0NzK81FFWjFw/emprGN7CpFpbrzE6uelOIvAuTHryft68fQ032/TUe42cmFV4gGPJjMf+4Cj7FEc+5Um/4RNAVwaNcQVJ9vu+hhsSq445u4uSVQJ6cDK3+9dToMz+7NJyFFrLYMLOlLL+zeQRjgm2AeAPY2MnvsGtdCwr17kLMu4z6Xk49pl0djcilesW2jxjSK0BXavPtMoqLiOV8f7cY3YN+Xpycc5MqRCc++Q9XxvwcN/OC+2jiFza6Mt0srSaupvEukC6JcMCx0t2xY954h4o7Nzx2BMzUlZfoFmQmW/+nRM4zq4LCt886gwhjwzBu7nOYQWp1QJ5rKhS3f0pTCb5z1nFRXUavQk7ms+6lZvs1SEAjE1cS32tylLGDiG/imRvjvpsyuaqLRkqSSS3cHyiUMv6pgIPx+xwLIoU/uCcEvrmNp5phcE5J6YPX/aMQCKae/Jb+voMr2raWmP/+FOpEDTnXGV4i2/Vzhb2vLnmBkJkIFaMjrp+QN6pN3IRPpGA4yFDbx3y9BNfJH65Lgl9gd46OUVKXYqUY2nsnDsBuG9eMevvhkuVOo6cYn2bBXTX2G+h2fgnHGRR8rlXtW169M7+wwd5Zwp69vnnVeByHhfMeACAd6dRdgik6+bcCh54M97xPc01HO/P1vDSsc+swvmNKD+Tx+R1H2y5+YtP3xZIRM9vfoetPl1btVQqBCfeGstWg/TtlDVoP99Z94t3cv7vvR0v/O+h2Qm1LleOf4Xoq/P5KfvIZdY4bax7eedQ/KNwK2+RJI2sz9pBaon2wdXP3bnkkbuXhQfP351Lwn/99SO//vqRO5cEUa+GLQsfMu/RtR+Pu2vmwpgzXf7xgKsZjF9xJwAjF0P5l/hr/3IiAI/ELqe8mNElruGYC9NjXjqmRx4vri4/ArOBPLfctc802dBK2UaBRPTr2Q+8se4yWejnQfyIMKkQ1Jw54Wc7zG/fm5309Ibx/3kDPnfNuFFbzrZYl4erJeS0VBhUJuD4qXjqMlu+4BUAXolMdnrEyZSx0MpiQuQhp0u+PNqxkfUXrvxm7j1IucT/65ECb/33j13fRjctX5qWUsJgoh2p7emW4imjKCqp00nPuPkk4uGY+XIegDW7VKafv50DxAqqUtVhnGfKLyBuvuX9DT8muFt3usWhgg+nRfEbpUJw8H8P+3Cvt7e0qzpXpW/8x7dPCySiv3101w9DoYYk7dtvPC0hb5ecitQdU+2OVy92BceH4p+4g0Ms3pKhulSpm7BzOpq4tRe21Xc1Bclf9s3aX7x75idvp8ZdKAuSLh37cMKVCS8bYmM7pOs58psn2MDy2UQY2Lh6Q3OvO+aVFd6iR6Drf0GY6L+9wXXihubeVtGKpwQS0cJTq7eeg/E5gViRUeave9W+//xLKgSlCfGcjrawqfSLI4tvnfsPhEqjfhgX+6RIKgSpkWIcx/sLjtfGLiLMjedwYW5MDiq7Fm7lzTm+HI19dfomTkntVePLiQyNIyUeNi28atO3yvq+3uMTXtz99z8YYmMNsbH6mlquAlacTHzCIhET+iuHnT3r9xDX0jG/lecbnwTurutNlsQNFa4ym03YOU0gEY3bZjF+mBZXC8SKEV8VM4xV6HIYN44flgrBrgf+6vIqK4WpZedRzxEYRcR/cLLk3EXJJzBH9SN/1/f2BMzQzUE6JtyuwK1PrFjAgb6YxOItGaqKFovBf0z6ZkSEOcejWaEtK42gUK7rTrHwpeVzf7qrKw88+5BUCMo/+9gQG2s6c8Zgwo5xlZ6t8dPXCH1xcvxIAJ5Z2UidLPKYazjmDd185pbA3Ygc+Z1z5S0/u14gEf1l6aM1HfVkbx6bD0MmupOmyWo0B0df/69UCHLWraSp4/Ol+Kv7UTZrhEGfJC3IbSjCcbw0IR7ZODdcPo8T8UYC4wZiB8fE3t2tz8cu4RiLt2SoqEnzdl07hKjxXuKXPhOW3Rt3X2oHYoXwg7weTf+4qyivZu741x1SIZCNfxGJxlhnJ6c5xQlriqdfjfwIDJnzpcy1KoxrOM6q1jOMIsl8uvmtPOa0YlQzoxxGqPjFOzndatvaOFlyDq3ho0VnqK3k39T8bFI2ECu+P+lLFrJG+SWpEGz+8y/1vT3UZv08buxpjk5bd/eycNTn4cufWHF2Q3OvJQ1VW1F+7G23SIUgL/Z79KCAOUlT4Ljx09dgqIp/jFv05owVdn/zZHZGqdZPV1Jo8uHAIUXTqdL02xY8IJCIXto6pU3lY0JPP+fI4fbRy64DseLrPbY3vUMF7k6rTiZvDPuZVAiOvfmc/vBhKBrLLWG5OEyVSdgaAwBoDB+5huPCRvat0Xk4Zp9RUezBb49aPKYaupV3L3tcIBEtTV3j/LAdRJInIFac9z4x5ZkPJ0qF4PLCr5yb9a1EUVcw/dA8hMICieipDW/svHbQoamDYx6F23cfv02WG0xcuMNa3EDcbOXJoM2byx991CGf0NnZ2j+7ruDelaMFEtGja14qba4gSdFfBxdKLLkOiusCmm61eOdm9J10dvpkc3k5FI3j43GDBady6vQ+vPwY3kIYILswNyZv5xqOb3bY5C225j1Y4BjDcYMJhtnUGzFkcq83Yj1ac4/WjBaD0YzrTZjeZLnK1vi5aCf5KgwTfucMi8/06/EfCCSi1+M/cPesT3dAJfLfPy+iCtTuKpPlnRVlaCV0V98gC30+OFac9ur290kgfmv3jLTyi86tnZ/1sVQIEsJHGDW2KEUYjg/CAJu1nQ3PSMcLJKK7okZdvEHNd+tMtkCUvEcERZmw1m3obdY7cW01tHaHAsGiWTiOG/fuNcTGmgtt6aPKmjmM6Abh+O8rFvj0fiUh25+Dtr4BF2DTYIIRdTrV5uxaw6VKXWqJNilP49I+Zs9VdVKeJrVEe6lSl11r6NLA6M+se8WwxbIPzIFhKrenty1NXSOQiO5e9nhDN114gScWlQGxYgIlAovHnlya95lUCM5On+yxJk0FvcmwKXP3I2teQkD8u2/++dXRpcVK16bTxbu2oOXXdCXDoc1Otdkfzg7me1OKte7YTGvQkpuch/JPOtAkwKfVLZbQ2/5kdWHeZ+SUD6O5rl+F47g5O9sQG2s8aPctZca4MLkhNMVpKWOGcOKByZAVt2eqmNOKec3+kY6NJtxowus6TenXdbuv+JifafcVdfp1XW2HCQakN7lPV8qcGOzV3HwWOkOL5sK48gKJ6GTJOfq2S+o1vyTyPK1kFhRG29mBFHbKa9Tw+fQPsbta01G38PTqPy95GPXwnyufjTm/uVPTbVeJctKSp0BYXLB1PaXYdrjL13lkuAD6q5rHOIozD89HNNx4mVEGFhvJ2D5aeKARiBXhC8rYbtiuPQwzn576JmKGsn07YbZ4lcqweTMUjavtTDwNJnx/NsthK1avW/PmjDlPPDQUDPnsS+53cd1x3ZlSrd7bRER2VHR9Emg4NmF4mdJwoojlUE8nirQFDQaULsT1QANeOnR62q9nPySQiJafdY1fDj3aQ+yPA7EirdDzvpzi+2ipEBwf/7xDI0xOM2sUkfu/RggikIj+t+mtxJyj9DdiZvP+Z0bCEEUzXYcoMprxc2WsR6B3vWPuboVwVK7Se07/hb6B3G0P0NOWxatGE4Zi7205a9l3ZbFx1JS6RZn88lNSIdh6161kblzThQtwB+/UKefHsR/yeNuKPwBw690TJsZTYqsGXGXBUZC/wMExhuE3O0wH2H5bUhfhgWzNjTZ/Ejc5s5PvJfctnwQt2xZ4oUxAuRXu+qSwvddDZOsd9/1ZKgTVpzzAqEPvD+WffHHLOyQQT0n4/AIzpafs80ipEOz7z/1m6y6NQ8s4jte0c6gopM5yII8P5BBKMefROpVsvLwLEXbm4flOFwNXgEJZ/Xl6gfP2o/+daCsu2PvYPYS9+fDWghzUIKZUWozbWl28A/p0XISX6v/3tI6b/OKBgGOjGW/pNR8vZFkidrcsjxdqm3qgBsN//vO5hYWnVsPAFF+P+smks1cqvVAzPbO0HIgVEd/T7c4V79oqFYL9z4xk2L0+nWrdpe0o7YhAIhq68IG5J7+taLX7rqRpqnB7LPoybc6+QlONm7hu/bzw5FV65hkqD+WfRIg8Yec0jUFLQytOL6F9iPn7G9h9St35tG1/+71UCA6/+ERfo82iznjiBBSNMxy3E9DTTWb29RXuFn7AypPzNBxF+OMWjg0maB1xrrwfPmPPleu6NGaOXmL0jH64IAUty9c2HAJixbuxXsSDv96kvfU9GMbbZfIx9FykNyjZvZW+GziOX2+tkpxY/scF96P+jIx5bv2leJXeZhThsQWlIgthcVH8RvrKJjMesDduwBZeS693u+cXb2TdFTVKIBE9Gzu+tpNlQKSnP3k1Nb8H+d9XunINJat5dXD94F7EBqemvE79QjJXVkLReNs2XOv69YPh+LWbHJq7BYwTqA/KqTNwJOtxCMdGM17OpaULlUDujkuajFxo3GlYubKt5vbFUGX8nWxjaYPFZ7q6RUdzi8OlfZkdaDm53CKvTjkiFYId9/3Z4S6H0/OV8sl7P0MojBwWDhekONTxeGrS6/Y9CQNipH/h1kqP2khx04DSV+y9pvbB7aq0uQJZqtz73ejsOroEMVTSsXv81roqIFZMkTL9AKJ/et7G7xEWn581zaGmcd8+uIOXl+dQTj3tUA00q5sOlXcvaSo16I85hGMOsgv78ul6qdILKKQnFpOrSDk7ac8nqLL4B7gwvHWXmrO3HogVd0wvUHY5ev4ce/M5qRAo1ix315mEnOT/bhSTQPzhgdnymmx3lenLz854VyoEB0Y/hFMT/Lm/x2TGOd0bcPfS5ai8TGn0AY5xHG9Tdb60dYpAIrpt4QOnStPdE4yrK6QckO69b5FDn+RLJQiLr65c5HDJnJcHjdv27XModzg1mvH+yNLkC1YwYaRLlToNV8IxzhUcnwiUppgJBU8Uuv6ScuAb/0/nnvxWIBH9c+Wz7apO1Bpyl/r1lNw+rXdvVOTa98oquzR6yquZUiHYGPb/dF2W9sk+d6i7VqdvGvEtTDIikIjuXPrI4tMx/nwv52/5Aa3D1nymaI5heFXbABGQk/M0fn6Qvpf4JZqLXdcOkdMUsAP0Rn9mabk/Tzz3yXuIBwrjpI7taLWGbdugaFxpx6KO1YjzAWOWvjVDxenXNvtwrDNiLv04mOAmd3X2XFVrDJiBfbdGG/vtzz2Glp+swi6u/LNRMJ7AquN0biC2VqxH1S26IZF5QKxYfKjRWoanTXtHKgSXvvmcLMFxvKip7MsjS347zxL68rG1L2+R7zGaPdhmUFtwPm7MgtEwpELAREPtcHtQvYl95qj6ThZ4ZfbxaMQSMec3O1CJ61OV1nzbh/lArNh5od2HZxlUfSfEY6VCEPvHn9445uJ1YsrIgKLxCUYZD0xm/Pz1fthA8nn23d14pUbP6XYUy3Cs7DG5G0kwlCs5s7goa6lEO2ZrzjvusKEsk8M+sTmPMlweh690IiXysewuHMe7qyoRRHZVWkSeM+UXxLssYdEJP+zIE8VnGTZOU82gViWM+odUCC58PZ2mmrtLzT0hrys8XaL1UzQmibM6fRNCZMkJt/olsjK7BxtSW4BY8bfPvGa87pobB0b/WyoE8f/8U6Pcha881tZmMW5TMhUyerUhb/G256qauZmNb1PJJhzrOElhybIOSMuN3mfMpokCiWhKgp3cSk7JvbNgGnYf5JRv9jUAsWLoR/n17frLC76SCsGZjybiOB5/9cB/1r+O1rlAIpp5+Juceq9XHdk9h4O0jyZJheDQc485lDM8xTC8X2xpWHzfs7tXs/PaQTRTU/exFuyJ4Vz8ex6M4LosqYlhfRzHldfkO++/E5qZP/VAp/XF73C76dQpaNx2HoZXZfgzY/iVmtA2sShVGowsfDLREYxNOPbZ3ZnFheSxqd1XvDDzoqMc5drXx5YJJKIHVo3p1vZSim2HsWegnPLo/FJbEeOjF4iEweOW5G768y+lQrByw+d/i4bx4WB6pOjHl59dr+xhM+54njSGUE//rL3Yd6uAHm0IC8gXK3SsC0GnSmXo4+nlbe+S+wqMWcD3iscUMJrVzyZl17frmbRSlXJk45/+H4yW+cYY5/0J1IK5uhqKxps2YSovDOp/TKeiMYSwgJzEma0xdV5Yg+OzoeMje7aMTVuLPdlJCBwv3nDrJYFhOFLkubRdo86H83Fdu37oR/nPPwEl1i/+9Sv0rP9seH3H1QPOlf0sachIR/qQssQd/jRlMuMVLSG5p3eiUMs6FiNKKuryfwwMIpCIHlv7clmL5+0vf+hPvff1mBtArPhwy01qocvj4p2WEFFp0+hynBsPHoQ7eNlMN3ipz6rvCmplpjthbnumqkvj3VY8ddTMj1mAY70JU9SG2GeIolbPitteUVP577+5VyARrb+0nZ7oiw/C8C4vf+f1OjxenPb42inRf4URvkeJbxfvmn6m/AL9s3y7quvp2vPwcLhVOPdT31qg3oVheG6dwR1/B2f5ziy1mkGECuowvTq+CWNyvimQiIYtC79U5fbl7VWbHivn1qjRDgRNRl0cxxUxUehNTB8+21xYCHfw9u71+FyXFQwmrFQZeu/p+i4TpwYVJK38hWO9EeM0qil367a4yeD/JumzRMTb9/fBeK/0v+ZuA1oViipG2hKDybhZvufRtS8LJKJnXh8qFYJv/iYc8vHRmlY2RXtqn1PfnyAVgqSxT1AL/Ty+WBFK++lcRLB1IKDGoB2/82P0ieODY45DawxPPyMy6r6wwm2YfJT5kMjy9R1dmwaDIT4eisblvtvPGUx4Tm0ovadLlUaONpycSe0vHLf0hrCWkJoDzZk0Hku+OLJYIBH9+/sX1XpGKRimE7lK39/kwWf6Zmf9otMxd1hDX4749pn1j/5VKgQzJkYBsWJMtOtgxB57S18h54dvYZ6nO37ZUc5yTmLWQyxy9IauaTf55vRBT1iXV2cc/gYh8qbM3S4rsFvY3mtEnvf7M51SSWFY6vvjGWqoTHI5FI2Pehe4yuVYQkW3melN0BKXI/Wq0C84xjA8pI1M/VEU7rh6AK2oTMY+b4W1GiQg17a53leR12R/sH82alYgEf13ozgh50hN6glocjQiTNll+PP0AiBWzEmwBXDxarLdVa5LP4MWZPnBPe7q+FyuN4XABs6VGtcz4vOoPd64JPV7NNFRZ9Z6rOx/hZgTzUCs+OesYmpT6tbm5HFPS4Vgy19/c/Osi/CY1MpYZ6fFuK2BnVgcwf+ePpzLSMyiUsnPY7/g+GZHSCrmqeJVdbsv7hJ5DcW/mXvPj16w3kYcf2MN3FeZm+jI0IcLUsZunUwC8eS9n52vtMSVR9b411YvxXE8JbcbAfp+uZOY4ysjaNvbdj0Ipe/LCzgxwzKYYDw/Ks2D7fhkUYCcNh2mKPbyTjTjnyQtcLjExel9X0NrS9Idqb2kAFmX73n4b0wcL01padC47ZyHRApe9Xx7JstmrCyyVhw3+T7o6eM7HJsx/GAOy6H+WaQmw6YOMg5oS9IRw7AnCZvfjw9KyEKGB7IimGLy1vdytQa4UavSq9dfin9w9XNoWf5xwf2SE8uvt9rSnTVnX0GeUZr2NvSIZUlNqIUKluJ1nZryulQIjrzyDMMh+FDNYMI61eatl4Nx7cnKudLFMyHUwfwTaOrFu6Zrjdz25IAcehX95t3c1h5j3YWz24YPgdEyX3i8r6HOY1ex2lqLaNztNl+Mx0ZcVjiUG4wYciA70HIxIo6PcIxheMlACd9VpvROQP4kCSbjeWztOL3JMb6PS4ZzKHxqMYxoPO/QtbknV9y2EOaHhzbLq8esu7S9T+e4y3duJgzic1FiCUiEmkKmS36GI0BNKWKWocwOXVVu93kc+u/zqd6EBdv36ZUaL8IZ+zxw+hsv3Mj6a9RjAolodOyEui6bQzz9Xb5dfZGwYZ//6Tqkm0qZ/JpJz0hLY0xKgqLx1au+PZf+rmALMJTST19LOO5rCCGdEduZ5WOOO4Zya8Cq7cxSM0+dsC0rEQHotVq6oII0/Lf42JlffGrTS7y45R13WS97aqvRsnHYXmvrNd71SSEQK77a7VmuoenJzbQU1H5F8n6aauxeOl0coCwEHvmnus3IkYmxtxQraa54eM1YgUR033f/ZdG70rkbWRWqZ8O/RJN+/quPnSu4LDGXlMAdvF27cDMntrdGM36lOlgsZS8Hdu/OgeC+SMdmDL8W4v6ODms1r56RnHutLh9h8dasBAc6MjlNzDnyv41voRagNLT+08waBc2NmYtn/2hrnPr+BOc6ZwosIcb3ZvioRFY3K1GGp8wlc5zb567EZO5/Z9ndV9StHGRl94dobaoOtHkwdOGDp8u8cD726qHyqLkIi796ZSbTG81m465d0LithGWTG2oHDCbsZnv/b0RVtBg5SvNBHSzNsS9wjOF4SPhDO2Auzemeq2qPIWMMJsOoH8YRASK8S4bWqe6KOb/5nystoS//vOThsbFLf/r2qccX0mX8NahVW/4ikApBw2XXi3PlUeWPOzO/mpxT2uDLTtTJSeOkQnA04n80zMHRJYMJ+uz1FwvJynVMspFyNHb6Zt9N/AK9rXcrDtPX9OGq7NOpCItH/2cOECuOXINxqTz+TFevQtE4KcljTT8rmIkUbof7SZWclKdp6TX1+9eSL3DcpQnqjXIa2KW51OMphtfHB+cKJKIn179mxph+shUry786uvR380agNfbImpc2Ze42mAwGE4aCZ54pcJs0OnfDao87bBPWwtj2Ty6ig3WXi+TqysVSIdg2fEhPLTsJI1w+hb7QYILbDzvkgVN5nSnVdqrN/Sv+0NMEx/HZx2H8E4FE9P35LR4rM6xgUKlOvPUSxOI//KTy6MHoZLgb/JDEs7SL9fRYdvBqaxk+y89qRjNe18ltgmMHEEjK0zR0mYz9mlqTJJovcKwYcMmvtmSoChro9BWbMnejRZJbX0TSjuYgrfziW5TQl69uf/94cRq1/oL9MFTbq6vd+kzvfuhuqRBUHj1IvcvhuEttGv5ZERArPt3hhRK5+tRRJCXdOMG+CObQQ4+nOiOWV2/YxrHRRWqJtl1l5jocl8fBMqywSrYRMZvkxAqGt9BU67lZdfC/DyPT9cZMi3v98M8h26w/7SH4lEkmgzt4Z87QtM/FJTORxyBRwe2rOilPU9sRON8fJoTyGo5NZry/PigcXmvsnibladw5ZclrstHyiGcQtWfHtYNPbYhA9QUS0fRD81wmTGvstPhM59U4WlPgOF62b6dUCBIfv9fjFKYXQ8s5IFbsYBZlvK+hbvs/bpMKQVa0dyoXjz3xuYIZg7G+rtbod11hf/mllmhb+4JdInYm3Y6rlpicTPzvnW8nS5SKrJ33/4WIlnl/Z4XtK2r3pXYgVvzhg7xejduQkVhjo0U07vBxf4Lshm8HJjNe1mzkIt3XkXxNTXuwbORSieM1HGtDOUoePYK7DGGhNmge/h7uen+evJhKOIdjZW/r8rPrh0c/gYD47mXh0WnrmnqaHapRTz/aetNdqK1Dzz0mFQIXGXGo91uPkcPVzyZlF9R6NpY8PuEFqRAcn/Ci9e4Flk/bAAAgAElEQVRg+R+p7dr6zJlVLODy6RJtdbtRZ8TcvWKDZdju+5FSIvvD/H8JJKJx297rUDPS8zo0Vn3q6Kbbf452CLSdjpA6mkhSQ5PF0Xj0KBSNMzMdmg3wqRnDe7VYfoPBf1w+kq8pVRpVOqzfdcTuaOg1HF8PzcCJ9ECMrt5odWGAHLn/axjER/qmOwrm1BfNJCyRERA/uf61+KuM7MbyrKG2Gjrs9CQ3z56SCkGc6I8YY7uiSeurgVjx2Hyb+OOyt1nR30iFYPs/hvY1suxm7fJx/hS2q3zB5RNF2pp2o8GEhy4KU4l2rTYfJT98bO248pYb1Esej4t3bUUqqbSP33ZZ+XyJ5buquN7FW9xcXg5F4+3bcYMdc7psKjCFEJd1WIH3uHwoR1PebNQYmK+nwAzIxVO8g2O9EUsrDaUYXf+fvSsBj6LK1jXvzXtv5r2WmaFVdAZHRW0X3EAFFUfEUUFUUARaXEBFEVEQRGlBBMSAKAIiHUhISAghGyEJkJWEQEI2ks6+7/u+L73X9rx9O51Op5eq6qruTig+Pqi6de+tW+fe+vvUuef8hwoKG+rEl6pN9nmOXPURSER/3fpAYasZpIssSXjD5yODXWL5ybVxNF2UXtdx0X4bPCpmGvo8jE3ca2b2hovkavy+TSAE9lNvi1su1RfOwvezNvb8cLtx8D9OkFqMUGiIPiXeNoDVdmMlrWhek7aiA23qw7qG8EEVoRrWgl1W62Es6LqeprlH3hRIRNN+eDqlhmoUhuwAiO6RCpGU7Zus3HrV0TpELBP/OhIFaqiMnj4NnNsKWUsxY+jZ/gOcAJnNURzYuAaUeMcg3tCDlbWj2Q3asna0oQfrHMQHVLhKS6A4qDmOVgU9ONZiRKgTAqNbdu3cMGP6HQiCIJPvvW/h3k3nOQm3Dc1RGpMgX625BqHWXzbKywfFMc/007N11JcCiWjSN/dujNhpFq9trjzoPjz5wzzDfTvys+GLpOigmoUM3uVq2RA0Insl6sOpje8+UF8Lg2Izf7JmcjFuwh+7iAQUGuVS3zW/+1rcILk3rMAG0Q9Jkle/WQ+XUM7hfdYfoaZDDddMbN6o0Gc8Oxs4t52xto1svWf+KjMJ0INjDCf9HB2MV7N2/hQEuWPGCve1h6PX79wxYyqCTJ6/8jT7iOyXMZKacEA1BKkkNp/fbZBsQ2/zzrgDt33/BITp+36c+/Plo3bm2nn6uzJELDsco9/jTtywGuQM3bzWcFPqB4djQAqoP4izc2pNtwfPLXlBKkSi3n6Nem98TZeSwKehW+Gq80izxroXtxqQVkuFSGmAD5XxfxcCsiI8tX3k44+Qy7UeHkA1rnWaEySVkU/IOvTgmCBIw6e9gw589t6GIDO2F4/cLj76xckIsjDwUAr7iEwMR4OsCgAO+S8cfQvOekZ9zschI9SXz7svP50dzsqCOJ0C9rjv3QT854aaG+C71F3C8CNxlTv4/Hzsm9Lh5wBjTNu1RSpEfB/8B12Nm5UH5DthSwI7Y/WcnD9c/HVsn8rOjohFzwG2zH8K6uOjx1YwW4JixG3rAGur56VOWAFLSgI7eDG21XCzHfKF9kiAHhw73q1iz85Vt05etDp8FPJK3kcQZIeEAziGtP8Hk44LJKKbtj9c2l4VVhDzyvGVBgPxu6fXX65iea/5Hp0TaGBaD4xhjVn1JuMZVaPEQzoeRUNutMrwIAjx9QlUX1HGd+cbci0BaYqek3N92HfG9+ouLQx46n6pEDn12LSOPGuR98at4PHxxC5ELJv6aYEGJYj2dr1zW6cencfW50u4kwA9OO5VcBCPF+T+IIJMmh+43wCvsGRFtNRQMuqgZf3rXMFxrwK/XJkGwffDoM0zfpkPj2/c/tCWC3vo7m5TnLaDUYAa/LntBcfv/ItUiDQl20Upm1EphwbBo/GdfdUVnrdPkgoR2QE3ioPhq7m4BM7k6Tk53/Jbp0YBH1tT8iXve4SALXP+U0PNFvdyrTwXtJhtD25GIyOBanz1qpXK/CXuJEAPjjlKBLtn+yIEmTJrdw2wSCSmLZ6OINN3SOJHacQjxopQnxkIMuk9S2BtodUoQLdYp7i1W7T3WYFE9LdtD0AgfvjnF35N9h5U08thTmvCVFr8Lx/kQqqtsFf+Raut2cruFzshIvu9+C+pEIlZtcRsNb5wnErgSlX6P7+fBfPFZJxyh18/0e8uxtQMGZPj8gEj1dLPLwLV+PhxUmnG9W2cymp8DZseHJdxlQW2ePX8KcjkNeujuiWrZyPI7MVe3SP4a4yk8YkArCcvXx1qEVLNNzTuxPLxU78uM9glFni+cyY/0jHT+U1g847bbpMKkcqwQFbuuNqjbsmMt6VC5OSjd6h6ulnpk+/EdSRQ3Fbx2IGXF7xyE8Tiy5vW2Dm2tw7X5P2sS0uax5A51s4B8M1p8x3nN3GWAjbUZ9ZkZNLMudMQ5MEtRht3xtAZn7MSeFnMXuxhAayNKzM6/vzsQQjHTxx8ha3NOirrLM0HREV/d9vthRQi66h0WBbiD1/UDV9a24in0hVfxzUlkPjdRjjFb82/Ja5MT0bBeKitSTKtu3vBfp/LxYOMO+Eb2ikBetpxbRdqj+5pve3+nYuAZ/Gz7nvMImls4uKZOix2b7Hejz1X910KMGjHAolI58p2zE5XNiozdPblOb/7tz331Oa1luM4qPQD6/SUl3j8489SIfLi7M+pMMVQ75mv6SISSNzwIcTirz/RE7f6Z4/yjqc3TpVK6+WldXdf9vnFed+X02vL12ZPAvTgmMsclC3rX58C4BiZvdRnjCEi1GcOcDdevvI0V3oxBPGOQfz7i4cEEtGtO2bM/GUBhOZJ39zHONCDykw1XgGJnD3u/Nt/Ls9AxLL2fnvDUiG4x61e7nlJb0ROLR+iMhK+jutLAFXIo1a8CrG4KiLk94yLX53Xc3IeTDrObPxYSorW3V19/sJNH+cjYplfMm/dYiZIe1vRg+MhNVdJ2g/tWzMJmTJra6BuH2/vdmMFWWfHQKavWc+NvdhYmx5SA4fdJbrQ5zd8PoosTnjDZ7VBX9aFQdv7VTh2xqLfex1QrO359rWfqhCxbMcZuxKmJeuCsvyfuFszAEKt1noBoqIHvixWaqjSNI8dIV/iIhIYaKg988ITkHjEOC/BT8OcnN9E/Uh3qERXl965ra3tSByIJLprAyUWWbo34uvblAA9OEYxbsJAwgOB4Vjn6yb12jENQaatSdN7ucG9u+lbNkWNUZmNIZulY0iJ29zfNu2HpwUS0fdxB0mSzGkq/OwsSFcK/z7z2xsUSYJsSp8kya6iPKjpyFubY/L6EbHspo/zGUfZlwb6wN6aUy4b7j5rWykilr17hA+yMohkXB60Z1/ze+R2wJb5r4d6K0pNnsEnMwSuz9XBX5lcsn6KxcYC57Yr+qQzj30DVssPYa3WW/FXuZAAPTgmSdKbfaZw6FaxfK0+1mPYuUK3X7d9/WwEQW6dt2Hpur2j/4aZNzHbgcveqSPebNEliXBxnyuKg3KHFJp3uQGYhpQuOgpNGwTeNufsyqY1UiFyeePHsObsb0HMtDSOSbddxflHp/yXVIjkuR8wvm9+veKPK7J/T+p+IMoa4adxE/7Y1SRQG3v+2D/+BNgy3/i3JVeZqJJLwm8fFEhEi7w/6FWOoqGw9Dh4XR1QjY8dI4b05qxzsj5ELPvvd3KaeiglmbbUM1/OQAL04BjFSdYp+vfsXA7MFNDpGIJpfOLCe6DrcfHqZ3X2ZDP/sB+VFyhTGDO67Un4TSAR/fP7WXU9o+gofTND/vXbCMH8urPbzBLMU5kMeVsLVGa7CnNh/ZNJIGZ6+uZiKs1N6oS+OEsqRMwSKvpcAZFXiFh2pYTfNzcR2zg4LTnlBdfJxTVvWx9uZkPefT/OFUhET/66qKLTDFWbSXP0zBlATyEbFcgHiQbXHK83qcyfci0BenCswYhzBUpjY+tEOj5XoNSMTpm1/ORayP89dhoulieLjdIvvX5itUn6pbFNxpZc27NdKkSi311sfOnO9YW/W3vPZPQaF9o8Ttq8VipEAp66H1WY8gfBtp/7NCBi2T1fFA1YTgBh8y6Or4DipEpLKLUECOHVkW0OqYl+Ja7SneI6osWJwW5sSbayA24Qi1O+3WipjnF5XU8jzEdzl9vTKbVZxpdMjvHCQsDc5m/qCpk7zMSdXuGiO8AYTmowsCS0GOCSx3BSoSG65bhCAxIOQFJWw1WTp3blU3pwTJDOz8rO3Q/AtTqNgUIIzln7YNe9uiC97TE/m53ForbyTed2/WU4Oemsg694pPlrMUquETiq9b4bxLY2XB6Vi2z/BZAi+jk6/kYlp/Rc460ZKWbHCQthLKxZflsrrRx/SYWCN02uIcra0dQaTXyp+lyBMlCmGGso88tQhOYoo4tUaTWa8na0Xwm2Kxlb3h3/pDbveHXrBojF2b/S2KOTaxRv+n4M2V/DC2PN30Wr1fro4j7KzXi2bfBtRMSyBXsrzbd1UilE2JouNKNWk1iujixUBWcrfcZkv/VJVwRnKyMLVYnl6oxaTWMvBn/InTRqGrelB8ckSfZwQVthh8GXRXTukZvxPbhYngSNxWfzLVLw9Cj69l/2gIkbBBLR1F2P74j9pX60iWPsnBR4HgY8AwueNrkkV+OClbnUDQsGiuQCz8MmXZmcFjcp//xuDiKW/XSeHpmyST8cnapRQo0SLf1YSrUmOJvhR9iJNPmVCnVLP/jM0aDGxHYcjZrDbi9+JIZYXHr6BIPbrB3m5PRMPz22OZaeDlTjc+fGXgI7zIPopPfBIgxON83qZLY+d4UYTqpRYkAFUtxG5DNcFZ4p8thiVW03hurUau5Ga2fPtOGYJEkuUkyyiKrMuvK7Zv4bnyRJmNn31h0zq7rqrIs7ICfi30fFEL4FEtFHwV+l12VbahLw5H1SIVJ+xvRTEWR3P92EiGXLzaVpMOmNIPCQeTOlQuTSZ6tMLpk9hTkrEbEsoXDAbAWnFOIE2dSLXSxVjdV0mM0mbAXewC7MQO3vlEdjdlNlV2fE4nnAG/22/6u7GMWsE5Ikd8T+AlfjD/GjfqqJ3l69c1vzqEw0xjfaHwmYrZhtYxj3w/gYw8leBcjRFZDFZk7b8DxlWRv4oXZBGxdtOMZwMrF8AuZnSixXW5met/0/F0hEL3u+S2VtXalKfy/gCwMoL/R8b6xmDXkv/R+/22yHtcNpGkqaVGYrGAovf/ER9HzCtVT3wTf5ge/QOz8v7BkykxvQ0LNjDlCc7BrCo4pU9sCu9bZBMmVtF0hj6pgnsv8uPWVFgU9PB2yZM+/syLVm/KVyryMpvnApbgjfYaiPxccD57ZLNrgDp28GGb/2X3D0txSqswVfrdJYn1l7rnqnyUtaUSuvvEFWjjygDceA0K8Ps0cQrtm2ocdihnOSJLsVvdP3PS+QiL6JpGrCq+yslUTuvWn7w/BleHT/S79dPSHX6HXw8FefBR5pR0d5pBlPPMxj9rmPNb7EohNH4cdsuyzDuK3N47m7yhGx7M0D9LJh2uyWbgW5mrhc4aCf9rA8ZUs/prU2yXSHz0n9pquJ3vfcKBUioS89OdjEjm9DSN4FuAhXnPpMg2mJxka9atxvwxkuJL33d//IG97P7Rp00C83hpNajMxr1HqnOSLO4FSmoqIDdZ3vJyZwrMUIL/a9jx0hfUu/BF6pcptTkliZCtd0UC6N1J9yjeK3qyce2f8SbHvz9oclkXszLpyWCpHjd0xCVRaV32tVgLb4P9/KtvQmtMsyIBYXnThKFxgqWlU3rAKWQbdw53j7YziZ16R1/CqKKlL1KszsENAVIEf1K8MC4ZxGv7MIs7w2GNz9clUaTCr276NixZlgoBpfu0aln5d/rETEsvW+jVQq21lHqSUqO9BAVk0Tll554/KQbGV9D+YKOw1M4BjDybgSDr8ujSXlmOO4EhWVzxZDlpCyjiq6K+9sfvRCz/cgKH/yuEAqRII3vWe9k4W6N+H7s2YQE9Nogv71EIgf+eIj651YuhqU1gM9kWNGp620VJ/F8gEVwXinjpX1UNisRUd7NLL4dIy7yvf4FWKxISaIcVdmGxa1lc88sODTH5Zo3d0VJ7xJnNLPUvpwNoOxCRjN3oVxoQYjnIsqcSUq47ADxg9iT0MmcAw+3uUcpAVxnn9FtzmfCrNiXanLoffiMX0OPbN1rBSm12V/fvB9+NZN/Wzav4+KA3IiLNWPzAEx07d8kj+2wqXPVkmFSMi8mcAXl+kfuGE49dMC+0mLKA5BgxINPS5h6bpcobZDchQfl0a1jB+2wVWRsedbGs1oVu0Y7Gx1P6h1d9/844qL5ckUW685DmhPXt/PlWkL1W3ZOfcXGv7Mh+UpIWsNRcmwXo0hHKM46TCrHysKkZVOLleoqf8qDqgGH9n/okAi+ur8D8wmI+mrT6VCZP+SWVN3PQ6V5ft/fG7/ZY8eRd/YDh/fCggEjiWMSlwGPeSkQqQj36LbxtiuzJb8+4cKRCxb9DNtZd9sb9YLMZzMqOVwc8bKFJu9dCZHqdLa83Nm/XFpXE38AqQPlwoRm66KNDo1VxXLzNS6u5d56FOgUmT0bu7R/vc7wD/ynMzMEjV3HxplahQYKMxOkLMKOwYpfTfQeEjKVRnCMUmSgyqu2N0cPA2DKnoKZnLNNQijDBhmFZ3t8MXryJNpMa1Hmv+sg6/A3v6y9f5N53YVtY7yyT9xGQQ3P/x1iWFCWzNSYA/FfgzZFA1dkSRZ06H+24d5v1stdoXaRSNn3KelY07dJxivmR45Tv3H2NKjMS5HlYqot1+DE1oZHsy4HyoNiYEB/Q5eQ8Pmc7vhqjuU5EWlrVt4KyKWzfzGlLeISlvrddJrXOgX2rCKytoctHVpIhzmcIxiZJpLitIgUyoHaTUayOJmIhfrp79d9YH59Ipay6zXNLmauW+nVIhErXjVuPxCcfzrJ0ZoPMV+nxp/SP7zM5B3PSwTxEyjCjlMGJy0ea1xD/Ych14Du+e/v2wXstnXfQwDO3WNTddRKjNLvU5JK+oUw8VgQx2kGTlx783GDHwGobF7gCUmgh28i/oQ0H2X3CEib43aR+VGd28oYj2VgXONxdZXyKUyhokHqQjTUh3mcEySpFJL+Ga47mtmXdyeKXLfDIVSS081NshxddBmgUT0nHSZocTmAUEQJ+69WSpE6hPMBPhlNxasO7sNviECiehfvy3xzQTk4j+eAzHTL/xQQZJk/CfvABeoF2fZvBetCtuCmoGRek1+MzckXq4fN1TViTo4tLo9J9Pv0TukQiTwmQd7yke+fmhNHPXKREuLXjXuGYmy87kWDNfbRxQ4Of2SAbnVTR/lD6nY8RZ0ZSyG0JHgcES2C45RnbuSTdRz2Qp5TVrGH6oKjfKxAy8LJKKNETspvhWFJ0DSX+tg2jrQ4RZ/GLItCySiu9zm7Ig5/KdVib+bLMJ37pUKkaNT/thVbGZzj+IYLFVbsBf4My38kX2OgvMF48MJh/p2riUZUi+vi7vgMRVkzzr3+vOqni7qDRnXRM+fB6pxWppJD5HFCZO3TYecnH1KG4Ga83aDnYavT48iODTpkMophpMp1a5ooxiLVAXNlPhnqDw1lTp2wfHvUZgEQcYUj4/3zUTWMcUqOz9R0+uyoXIB1Vib4g6c86BUiJQFnbRZkyRJn8zgZ357w6AsT1+qz8dTGuhDpTndOo3dmpt1iXm2B1uMmqXbJ4aTyZUOivIwmVwGp95pcsdE7pX4e0Nj8cWPV9AVKbP6eHk5UI1PnCA1ZkI3r9XnQp6sp35dVNlpLUfBlZJBaNcqaVIyGwkkeCpvd629O+urpX2Ana8BKhKzF45JEhB8BMnGmckiSKZg5d07muonkIhukIhym20wFFedPyMVIn4z7qQyK4Y6cWVJy0+uvXHT3bvuBLzym1+5P7I4wXCV3QPIO26wU9vZOUGSFS62Y279rfNMkZ/NZY4yFMWVfXAPxOKUbV9QbGJ/NfT0aUBqXFBgqava7oZ/6X7773abk1o7ivvYpMkq9zpELLOHFLBraJz5yB5Pkcs1DE2aJtKzecoCHJMk2accZyKGTIw2pUOlwidnJAKJ6Jnf3rDuMwUZYXKPmCfqtH6jkLdflgqRrx74M1SWZx961TP9NIqzv/m740wLIpYJP8qr6zSjRlkfpMnVnvHpmZ5UyaE/8tVtX0Aszj6010Rc3J3i2dmAuS0E7ENY+TOkli/xgZyc90UU6jPgjK1f3a6GCnJsvg3Lxti2JEkOqQnHRD/b/OmlVeFMjpJKmJjZR6ZVyA4ckyTZPH6ILBp6MRaFq8G0Tx56TSARfXbWogN/S1oSZOfSymnzeecc3icVIu5///Otr/n+ee13or0g14NAIrpt1xM7Y39p6GXNtgDXzau69KkvuoGdQ8Z/UJwMkjHnQqT1qrBeubqL/d85kiQvfvwWxOISf2/GgqXbkJDLtR4eQDWutWaFMHS79sw3cHUdTw8wFJocbA8Bv9lPf0fPp4jUMaiFMOVNZX2W6XYYX6pm5XvaRJgmp6zBMUGQuY1aug/p+Pq5jVrW99CzGvLhIvbKML+I41YD4tq0HfRySpIk2XglHr7D5Wf8X9oDdtv2hLeezg5/3n2ExvPjkK8z6nJM5pXxaWuf9u9rgWudJIDhjg1BkPlN42AlWFp7ftcUNglMaIlX2d0V8frz4Pd46v/WxUXSamtnZSwpCezgxcRQ7+e7mP1wMbuN5uQ09KDFiKmfghVy/BK9TciKcWUyHrs8uijH7hpkRfeANTiGN3bxbb2YYouUPXQFZ1L/eEYAXMRZDaZuD70VpRBSB+opaSiGnlU93X4zgC9UyvZNJElGZIGcklM/1VsAL1elvXt6A7ypQCJ65fjKsAIz/nOG3qgfROWC4GxELAtJp5cgCt5CoSHYpS0e+2JwXZJVr2HrN7unrBhu4frNuKM9J5P6LNhfk2hv1zu3dY6K6rTZM3SrF0hEXxhxchq3On4JRCdN/bSA+u8WipP+meNsh8lkmcWVqLhWkFmGY5IkrziKMtFEWDZPr1Rw69f92dlvQcrIQ69pRidnSpaslwqRhHUrjRc0leOYVUukQiR80VxD5RmSEkQs80oc0UoqOmq2RO65cZjGc8b++Ueu+ig09m5J7Q4DUVh/+SC3qo2e0NDx48NkZcF4pcrlahZ2b5pTLp8Q3aRjy5w92Ggjd4Fhltk6QKOigGp89SqDDoNzz8Nf+rdPfabFzFhvYKKv7SGUgjkJkixtG8cfTIal0slx/DT7cEwQZHGry4k+p1HLor3Y7PrGCQK6pn1yRmKooOrpcr/5j1Ih0p5Nic/Q0BAmrPS8/Ya+6hEz7vFLnYhYNkNiGjUwpJYfTvZ+5GdApiGQiKZ898jWqB+ruugp44Zbw4M3fqlGxLJ5dFL2gU3diZK7K6nSWjoCE1mZPYUZBkAQ5tuvoUp7fyDN3sJKIV5dDVTj48dJprdOrEyFtCovHH2rpb/d5F6x+QPwE6qm3fYPthYjxrtqDBH5bK5pdmMTsdh5yj4cA2dkkuwcwl3ki9UnXdHYizkm6Cq3uQgC4tG0U3BiZL/slgqRC8tfpjVP9QnR0L5RGR5k0vAfOsOuJTKX0Pyolz3fNVgwVgZsTKqmx0xvuF3nAHr7ZyCn9eZTVI3I6MTKFNNjBzOygeaJMQOqYSKYHaBBQWAHLzeXWXPYqqi1bMYv8wUS0SP7X8xvNlUCxL/WIGLZKncbWj+Gk0Ut48nR2KALmz3gaKcXCpwTOIZdq7SE0yOywvOUdBmC7Fm+JEn6ZoZANIRZ8nweuFUqROriLlDvVtHR5vvQVLD1t2vL2FaQzGW+1RS/abWy1cEghhv+feHYW4E55jNUju3fuORigV4DOp0yEllrXMHkuGNwnPk7mn3fDIUXS9XMLMgZbsNsmW7bTETkmFM8Lw84twUG2n+79sHO+R7vCCSiW3fOjC8fZfcoaVJCBflKyaCVG2mxcW81NiwJzxR5QJaCAcuNFfkYX+IQjkmS1GLE1SqnxWUlV6o1zmAZ/yJip0AieuzAy7knjgBW4ucfM5a4zeOodwDF17klL5it2TOE/tfb2YhYll5hw2eurqfxu5j9/9j5GATlB/bN++WKZ6/SRj4ek5tCxoz/XZlT2mxjF1SDES67bWD8OtE6ZuD/f3kjyF4oFSL5nr+aCNNBpyqV1ssLqMZV7PCm4gRh2DQOyAk3fgpImT1v94g9zfgqPK7rdgmGa1rzbr1yQy9XcXrcwjFJkiot0avAYx2bPSS2RNWjwBnTA41dUnRLnpMuFUhE+x8G2zglp2n4mWb+BCjfvO6ebMUNY4MvyD36npSSaViDaY+lnXri4EIIyn/d+sCX574vbrP2/pg87LJD4Jv0mZ2jmD9N6sBTdjP+Wn8lHHOVFtEiqlJGvbNIb2UKM7UymZUYF4VYSgpQjSNZ9qj78tz3cAkdSh7h5BxUYTfpYutPJXebfRYUJy9NuEzHl6ymOTYrB4qFnMMxHIcGJVr6sfA8zkMDwnUZKrn2R7Ep3MLWsjkrbpMKkSP3TrZZ2VChNvY8fJmrL5w1FI49KGtRwY/Eagq7KIbm54suLvb+EL5RAonorVPrTL49DTVNDvrk2F06csUNVhOmTTBLBYR76gl7BhvrQ1+aLRUi3qKbmq4mmsjQYadEV5feua2N/fTPP16SwvWzLXqEk/O32A5ELLt7Q5HZZ8QJ0jE/nI68i2+6wk66G7OyIknSQXAMb08QZFUnepqb1ISnsxSOp0m0JFaSJD1eeEQqRF5dcOPVGko+FUMtTSfumyIVIhkUDI4rDgON9UvKm2yGccoaCz4N3WoA5WePvHky64zhqqWDxGI9d8zJJPNKkEJDZDe4nDsNK68oFYccwJY5407Aljlnek+ZDfYSS0JmpRyLjQXObVeusNLb2E5OXAuCiwU8P8sAACAASURBVOfjkK8NV2d+A3LWmE2D2zmxthMMK6pziJOMIQ6FY8P89chxWb0mNIcFZTk0R5ndoHG19MCt10DOjiO3/PHGTXc/sv/FAZW1vQ4olgvLATHFheULDFKycnC1bAgRy/78Xs6AkokZq6W//Yf4X+/84Sn4at3t9szehCPtgyPuzGNvvT+yHRHL/vudnMIGMz5bapQI4/7Tx/AyOPKg3Zarad3FSI+p/ysVIhGL5ym76QVcjJWzPSV4XR1QjY8dI4Zs7CvYc5cLxfF/2/aAQCJafOLDft3ChuRT//1OTnPPKDpKnHDdKAQ7l1B6LWuBQsZz4Rw4hsycGE7K1URxKxpNn6IzulhV0ooqNASGA786V/sTv+ZtEE337aYXjr4lkIhWBWy0PsKMPd9KhciJ+24eaqaaQR2mudt33q5v0hPXguccft2gLH8etj232fxXJ0mSb/8GVPInt5shK1BoJkimrrFvaVqNNTal0tMnoH3p4kcMs9laXxi0rqKhoWAHT2aNko1Wh5YqZ9TniPb+SyARPX14cVUXcHR7fT/wUv/keL1Jk7HynBglQTIFY6p0ExEZnzoNjo0HoUEBompQol+Jt/ZjlR1odoP2apU6rkQVV6K6WqXObtBWdqCt/Vi/EtdXdobLhPGYrRz3VVfAV7S/prKsveomXcjcwSSLqe2qI8/C+rUxNNzRzuqSKt3+WaGVkVC8FFt2ZdnJTwygvMTno6iSS2PbDqnw+zaBDD3rvBtMrpa4XuAPW6+93zWLhsLsQyAhgFSIXN26wUQgjj/FCwvBDp6/v2NuXdPdAIOe7tnzTFqdLKdWAfczMirlhgH0jzeiR1prhouc0y4Bx4b5Mz5AcQDQGpTg4lfI+EasH6d8u1EqROI/eQf2HDQcb5pYmTr2XgMNtd53C6VCJHMf1awihk4e+hrETPtcsWZkMFS2eVDQUvpFxM4bvrkX4vKTv752PD0Aw0cZQ66WAiPJ73wF3kaB2iotkcp+1sTExQiCzPPZnyK3/JK0SDaueXAqgiDIjdNXLd6TI7VW2Uo/Ni6ZNR+nbAOz/Pv2QPbBPTZly3kFrVbr4wNU4zIz3y4c3X1QLX/D5yOBRPSXrfefK4pbr3P4edkooUxVpzOiP85HL10499bJcFUsenFnziFuVkVz36hXgxUhuy4cs/J4ju9E3d937Nb/kQqR1syRRDjfRO4VSETT9z3fozBND3p+yYswjpbBUI/Gg5jpJ7aymd+3S97zU+JRmB5CIBH98/tZu+IONPaNUBP8GgOMyP/xVnZOrQKOWa4m4ktZ9y63Ccc1a+dPQZA7ZqwL3HQ4bPV78ychyLQ1aVwgsmIM+/jFNSsgFpecGvH6YjCDbDXB0tOBahwRwVaH1PuBfN+/g/KhK/43vJ9rzDyVVe/oDExSrx3TEGTSzFVLd4ZtOhy44rW5YFWsiOYCkcvazVB5UJeb2Zo8HJsVC/NC+AF7/s0XTbpYoItdftv/c+PytF1bpELE98F/KNoZmoBvWZOPiGVROfSCO4zHYOnYPztsnvtygwVjTciWa/X6iNuV7rWIWPbY8M+AXE2cK2BhV3a0FmwDjqWHt0xCkBnbi4dbdUtWP4Igi1aH21B1h+vTqNZltI2u6uk+98a/pULk2D/+XBt73pL0HFlO9PbqnduaWSa/pvgU22N+huvkFeleRCx7cDPwLSEI8mKpg9O2taxdiCD3bJHEj0zuni3zEeSRpT4jJQwWgNkmmXUa1t3deDimuOSoVjv5EHA3rok21VOquupu3TFTIBH9nHgU9lUZHgw1rPp45sSY358FXOAL97GfchQOMrEy9R3/9QZQftVrVXhBjFpLPPhVMSKWfewJtm5UKBHIfnYuG3C8ffP8aVM3bEo0es08diAIstjDqISlr9S6bv1naU95SeAzIOGh36O30+WEorqA6NfD4uOBc9slM+Z++p0xbHH46gm4SG79ej0ilv0S2Y7hZGShY+E4KXHp9DuQ1Ymj0DPUZwaCzNhZM6qQjYVxpUKtZdtcwcMxw/VntlmJv5dUiATPfdTs1dD8KLhkL5Yn91VXHr9jklSIyH75wWxlioWdA+h/vAVipjOrR7ZQKLalXq28o3rLBbcbv30Ijn/mLwu+CvdA3kpDxLJj8Z04QXqnsg6Cejje5efz4px7JyEIMnX2nHXR+5Ms3mj/zkUc6UF5TcB/qznlyol7b5YKkTMvzhpooBQSSV3CjGsSjY161bif/S8kWqMKyj0Hl8ef1r8z6X1Zez96NpeDb6bJCLIwcJTxITF64WTktnVp5gHXZ+9t3MBxdJEKuhXQkpL1yjwcW5cPvash82ZKhUixn6elZvCz7t69z4a8MkcqRGJWvmGpJvXyz040IGLZ+0dtEGtR79BSzUHV0K/J3g///AJ86yZve+R/Pt34H+9GplYMmX8T7NJBdHA8ecqkyXPnbATW4ZXLgR1w0nvRZqzDSd17Du94cDIyaX6g1a0/i1BuffypNZqKMP2nTNSKV1Gl3mhuSVCOLEfDw4FqfI1SqBHXA7tUmQI5Uv78xWsfHs/lImh++7pHEGTV2qiRqdSZrSyZI7ol7z+CIPNXBo3Utz7X1K+G5rBPtsnDMWtLFIY4+07/u/UeX/N6X/zc3+AHr6qHBaeI4mFiLfvzjVofueHqmbzIBTqWr5u/uOu1+TfeumXD0WTWEVkHx6NeJJ1lENkhGY3y+3cvB5voCILM3LHdyGhI/b2yWfPM7oPQrHTy4X9Gvf2a6/zN2/Sp1t196NDBmHcXu8ioApfM2zzrJvdFj81fcNe6wAybsqVdwVTb7d60AkGecN8zelXAbvfsXA628lYnmvkJN1ef1mD8MhRmXW4MrwmDAx6OGQjNfJMLyxZQMT6k+h6G7/bPBz4z3xH9Usjys+U0VWJi+ncwbdFVnB/8yZtHbvwDIDz66Sda65haZR0cP+tj/Jrt2b5o7GbdIZ/o9Yej127dMGMqgkxdtTaUfT0oukgF49fhxLnIv+43/UfvXjetu3vi/GdcZEhwGAkvPKV1d8/6YacX+yYsuWdK8conjTwgY8OeQ5AHtxp2dEdm/9DBLcDLYv6oJURt7Y10YqW+d6qc38ozxQUXOW+TZYB8z1P+qOq1Rg3cU14CA2pfXXCjQCKKLmWHa+ayjlPihlW5CjUnofTGQq6PjzbwlgH60NefKz0fbmXVMr2ktx0bGx901mHLvhO6TRtTw6LdSpBnivxKhbrs3NnqC671t8v/JFCNvY671MA6T/tBW3a3n6/fNU6S4+mWgd7+cGjPqrG/0J4p8j07V90IsdjyZgPTlakH60CZgt/KM0YGFzpOWLcSRGd9YyM6K2whUGTiPly+K+6gQCKa5vZ0cz9DFzeTh3/u+3JELNsfaZpEx6SaPafFfp7QOA6VoMtffNSRm0WSJDesXfThOKVl/esIMsaaYedb55kiz6zTqLWuFYpPDAzod/AaTCMk7ZliO9tiqalwVHh2NoqRwdmsb+XpoDAqcI5eI9bZr0x29lJaJGt07sbvceJubFhO5wqU/FaenQuGk+b9ddUQoXorrcVEXf0G5DD1f/xuzQDYBIcRTUt8PmJlTCHpvYhYNm09CzHTJuORt7Vk/rjD8LXuc/8tmft2yttbDdVQjPTLYF0Psg7HNeuXz77xnr3bRym/NWvnI8jkHaMLKX14Gt4xswfFLVrqKZMNYuH0AEtMBDt4Fy9yehdanWMJCXosLgFpnFCcjMjnBo7h7+4T7nvCgRPbc/tajGatW7J6NoJMeXA9JwFBRjeSx5eyn92Ctx3TWnLmK6fu2CwVItZJZMoCfSFkN6dchr009bVCTrXvLx4y3y/N0ge+BL7AfhaIwGl2Bqp35GYlbvgQDhumNSk5ZYZ5Q6klWCHnM17rninW4Vi+Zytw71/o3m1oJfXQRWSZdb0Yhdq0Abq2i/34KwbTYWhCtLToVeMea5YxQ33OD9Rq9Px5MCQPD7xO7+GDEWQMfWoww2xaP9B5U8xdvHoNMnmLse+5bu9uyqwtXIXLG48qtUbDOn8DD8f2rlXt0CA0B7ekJ1vqq7u44Ogt/yUVInnuvxjXiSq5BJ3GzhexoOYciQNE4GYZ14xvSuW4Jir8wrL5BiCOfndxfUKMpYZyNRFdxLrDvw049kxMWzpzCoLMhm5wq9etum8ygkzfYRyRZfzy2HNsHJVnSQiOLIfYh6WNROE78u4m9yJ6e9HgYBCifeoU0T7KVpZYwXro/PBPqc7XGDFxfIwKew5QVcyds3rv0nWj/q70MNaghzux70caeqObSMPOUx6O7RQgmfPbT4Dr9vXnrXQE80TEr3l7bJ098b9Baoj6Xnv9IjCcuPGjPEQsi81jGBGAa7UFXkcC54DAM7AzefN/Jn29rru4YOywjUsUGi6y5NmC4xS5Z1LNpnWrpukohCZNm2s9SMQeOHZili9jOcNjvKIC6KEnTpAaa8yfYxtyUQL0dF9fgMWhocTAgMktsuo45KzQOSCPdjd236J3eRzzHxdReTUcfDO5HBzjBLA6oTgJiIwJ8BczOjWZb1c49Xv0DqkQsZJOKemrT6VCJODJ+7QK84FzkNxykfcH9j/OdyEgZnrRz7RzVg401KZ/Lzk+7a8QiP0euT374B5Vr/ncHybj1GJkJpcvnj1Iykpb1v2ZTARI6xQNCADMbQU2fiNp9cmsMl5dDW0maFQUiZqx53BKuwrg2HTzgB21l+Ka6bCVl4CBVJ0PxzhBKjREWTua06hJqgQcx+cLVGdylP6ZCu80uXea3D9TcSZHeb4Ach9rchu1FR2oUkswS7puSUbpu/U/qUsCjV0d2gKWIW4ZlhqRpTqLcOAzD1qqUXIKhE1LhUhLxqik6Mb12wc7RXsAmfd3MfuNyxkct/ZpIQGmgW7NZiet11Li175rsEucffnpsmA/m61MKjT3TbRswYZ3MjxP6Tr7eHh2NlBFQ0JM5O/4U0ivDLYTL+v3QsaOQYNylpQgPvrFyebdjQ0Tx/UB6zEgjs6VZzxhOEEOqoj8Zm0IU2+YkGxlfpN2UAVw2V7RZLghywIADDcFLEGWBOjNBuluOoi2AsdnXpglFSJFPnpWIOMHJEmyMz9bqguUsJnjPa4sCRqRzxYwpxOCd1/rVY+IZas9bMdMV4YHRyx+zgDEcR8ua0pmSENDECQ3Pv8O1XfMvsBZ9RrW3UtN1gnVU7lc6+EBVOOaGqpNuKmHZWZCvRjLsKyqkKQWI/wzWXa52X/44NJ1W+bMnIJM3rCJmyBMs8vApPB8AfuEFU6AYwwn+5W4PShsIhfPFHlItjKrXtMjx21udLYFLkF2p4MlOgp/3XRFupUL4BietgUsc0snASJbguP6+ChdRqUp5tc8QYQ8/5hUiFz6bJX5CqNLf0p0F0hEf985s7rLNMPN6Io2zgrqlVBBbuw2b1vUyofy3H/xf+IeCMQeU/835dtN1l30bNySBN83sSWs7+Y5H4s9U+QcJam0KdKxFbCkJKCNRtv7gz22Z1olcBjgVyE/32bD/Ga2E9p67QXxHdOWr/AZcaoZiwlclxQ2j8oKaFMOFCs4zlgBgTihjMOX9mKpqkeBa6zkbRpG27ZAN7fdbkALNuAySUJ7xWhjhTU4jlzxilSIZP20y6ysL2/8SCpEgp55CKO85bLi1GcCiehlz/fMdki9cMkBkLhsa5Ap+21fVXnKt5ugHwjwgH7injz3X7RyFtJcqlGiqIXtF8++jW9WXsjTmQp2bWLUJ9GkJtHeDhVSotN52VExDIuJgcPAKylxug6pObNXOHV5DKo5CQtyBBzjBDmkJq5Wc+b1MnpiLpWrB5SEBXtfuhuwReg03ww3twyyLXDJaPwFxmKjEotw3JGbBbTLG/+g7OoweXNIkizyOQp1zzaZta85k4bd8t4H9s0TSERbo340uUTrNKFwABHL/vpBniGWrCn5UtyHywx2iYjF8yrDg2j1abPyhHzxkirVal0iR5uPz3UFNCoKqMZXLe5AcD0AYmgIDQsDWHziBNFE1QsII0jW7RWs/NDa00mQTMnRquAcjpVaglN/F0tiTanWyNVmtvvSdyNuGeluu9OBmXh3QMAyg6VYv56BjgwNGqDAIhxfWv+BVIgkfb1u7GvQnn0NAl/hCfexV62XXKpMhUbk4Fy7Mk08swPETB+Kbi8L9jv78tMGII5f+25rRor1MTC7qsUIDvhtnWyvqO9hm2CckXD1PgzHj5NKJaMO7G1EdHai/v5gFzEwkOim5G8Db0kQE9DrJqNWw9E3E4dwjOFkQ4+TN9wbejGT3zGgDi+DGnFbwO4lektxhtswBJvgr8mpflkPNtZDgOspA3lojP/gWk3Qsw9LhUjiF6uNy6kfH7jiKZCIbt7+SHlHNfVWJjVPx1a/NHvdj/+8FY7Ta9pf07+XDNRzSJqO4eS1CefuZu8WscmsMD2FcRZ4rj45FtNuGLYj6uu1x48DLI6IYPB70CXHLelM47RcPiZ3IkPJjmnGFRxjOFnIuhV/tFGC4lwWNGtHufED87F+d85YEbbg6GYejtO/l0iFSOwHy8bIk7z0+fsgIci8GQTOnFxtZcAXAonoxWMrxvZvs6S7pCDp63XuN/8nBOKjM+4v8DqCa81v69nsjVYFhYbwSWd5J53iLHNRjTsliJZU8fx8qJbSasVWZbysTO9EwZQfgyC5I69wwsdTYrl6FJ6wJWhdP1zBcUKZgyzFVN7D5Eo1i578qFLhefsNUiFiYJ8wzEjB8d8gCHbkyQyFDA76VYMP//yiQCL6+gKN1E31CTHR7y6GA5AKkSPPzXtk3s9zdlhjNWIwNitNUJx0fPJgKguAQR3/TIXJd5WVB+fwklqt9fICbgzUts7YHQmem6vHYvts1q39Tv5KZrAALDVhncXNeMo4gWPOmJyY/xheKFQZP7Y9x3nuv0iFSPhrc006ab2WAqHQSnImkyZWTpOrM6AR+XR2uJVq8FLJKS/oVAcHkLjhw/acTA1K/O1DEDMdX2gavWqzQ8YVUJwMknHE48V89i29WlbK85u1LP6EM5YnlpICVOMLFxj3wLjhCFumzC7dgiRBWMAFB6cxZfQlbWU9wEtZ9RruVGNO/I5Pse34bVNGFCuczmQnxdmpx+6SCpHKiGDjhY4qFQFPPyAVIlc2rzUut+cYZuf927bpRW3lZvtRtLdm/rTT5/5bIAqfuG9K5o875G0thsrbgpoRseyNX5jboA1dUT+o6EApzojLVjubq3QFLCa6uqBySrQZR4pSnwrmNU3YMpl3NNyyc2giWJCtOdEOP6k9/7OsHfuyz3vLpk7km2EvIpeHnAIEFE/dbyL0+LXvwBzDJuWjTnVmaxjopw88yYBxfwgyEgo4qsWHQZsFEtE8d1MjdUee7PIXwK8Z/g2ZN9OsSt7Uo4EhIfn19j74qGFZPSFI8nwBh97lDgDxpl6XcKjAYmOBc9uVK1blzfZFtRq9cAH8DHh44LWs7f2iOHmx1IUMmAxWUV6TVsWx1yObcBw3HuKy4krsslqcXfCUVIgUeP1m/BLkHT0AMzN1FVmJUxqhvxjeQoRO0KAnEC6ojwY07piUaxQzf1kgkIg2RuiDTWqiIy4sB0n54N+odxbVx0eNajP65CNPEDP9yXG7Iv1Gd2n7rGNwHKtCMcUqV1CN8bo6gInHjhFDLMTp2J4zXQ2ir0/PlunnZ8KWSbEHK9V6FeN4VfhnKiyEMlh5YtqX2IFjgiQLXMOPgsqPXgFTs2BDYpxUiHjfIySwEe2pOfUKRMbSAB+d+A0gOwp/jUJLQPif8SloNRwuOHYC02plAolo0tf3eH23OuiZh+C93G/6j6SvPrWK/vqecmoVUEFu7eUkrHPsgGG6poxaDskVqcwyszq+GQru3JjMyspSIRoaCnbw7LbbWup/bLl1tsyx9emWoDiZ0zheQzfrujGuLRXs2I5RjKzuGmfmwqpOlMG+OfRbuLb3O8NC1AwOQPKHZMnnhsJhtE132+0G4k3IEVzWwS4yVhEeIdMw9DJ8MNhY7/7Jqz//Xe+4dvLhf8oOuKl6uoav2/5/8f4qRCzbHjJiU7bdho0aUexz0rNpuTKL1019Iz+0bMiAYR94URHYwfP3Z9iefjMQaXL0KLhpVBSp5eqXW4MRF0vHnyErp1Gr4MzX2Hiu7NWOcYLsHJ9fpi39phEixnIZe9xZkAM1U4VRmri41WKpEDn78tPG9fXYmuG2JDA9YHdAGwjtM2IpggRGxiVGvBnG/bRmpiZ8+h68qVSIfH3/n95b/RhB0g6Wj8sHMdPC1XkoR7FExoM2OsZw8nTWeHJDzqzTuETcB4pqfXyAalzmICdFiP7ATp3ITmpzo1VgekgQ5PiK3kwoc1ygvL1wLNcQgbLx9MoZdKJAmWJARSNSA1ICXfnyE8P6yjkM8oAc+8efTGPzmgKWLAtIDwQURem7lwQEGkL+hpsC04Q+ONusXlwVERLx+vMGII79YGlNYuzsQ68KJKLPw7YP90Lj/6e2lyFi2W+xZug1aPRCv2q/ctyYCy+Wqhz7a2VRmlh6OtBSIyIs1mD1AkW2TBbviRPkSdfe8zegREi2Q6PS7YJjnCAvc5cOixvPQYOgPVPklyvUFLWhoZYmCI5dxfrNusYr8bCkPOTUmJUKzMdLAD8nYIwbicke5scAEKyjVzYhMEIV8ryjB07PEsGej/3jzynbNvZWlML+MxvyoCeyV0bgmDvaKDh1tRsRy+7/0jSk20Yzuy+jGFHXPQ4MWQ5+66zIlejt1Tu3UabpsdKbzUu02DJt9ka9gnycML05mOraLjjuGv++hO3UMqxkuG2TCpGYlW/ABafq6T41406pEEn5dpPZJQh8J8by2Zs4uo2cwkQkc7bc9n8QiP0fvztXul8zNGjS+fH0AIjIskYrLhwmjfSnd20oQsSy4DRH5x7GcDK/ydU3cMyLzBmlWHw8MBokJHB+cxyHjnTAKlJRwfntRt8Aw0nXd7/pltP4eh79fAzPmMMxTpAc5A/mfK/GWDv2TJFHF9n+RMU0Gq9pf5MKkcYk/UsSs+pNs4F5DCah6eplaICGQBy+aG5lmDXld93ZbQKJ6MlfX9NiZrKTWRnAL5HtiFg2d5f5iBIrDe2/hBNkVaeL6shBMod+jVoXJtHYqFeN+xkmn7Xev+HqCFumtzd1tkxDc1YONCgxqHJdNuRehaOx2C7PiqbeCRKH3mjL5z/f41epEAlb+AxchbIDblIh4nn7DX3VdukU5SGnwhbOgSgsFSLxn7zTkm6bzRYn8DmHXxdIRGvPfEPrrVBo8Env5yJiWWKxqdJNqx9mldUo4YJbvjHFdjmhMxOFlVZoeDhQja9ds1LH/kuALfP0aWCepsmWaf+tTXogCJDAiXF6NhPViq3TgCwFp8QUJkIwPmWoHeMEGZo7EagJPFPkobYiYk/PBsbcirMBJEnWJ8RAAK0MY0jfru7rzf71R5h/WipEjt/5l7RdWwbqaGQ/y2kqgiaLY2ljzdbGk2t6LAloQsSypQcdGjNtGIQWI4bURIDL+FqkVruGH8WwgPDSUgCRJ0+SRl7twxdZ+59oaBhhy1Q4LlbT+gNcKneVgL1I9shtrD+y2asM4XgC8BIY/5bWdFr88K84GyAVIqdniUiSVHS0n3xoqlSIpO382qw0rRd2lxYmb/nMfcofIaAHPj294PhhTKO23srsVZ/MYIjIGXU5ZiuYLazv1MdMFzU67QtdixGu4I9c04XZzKxoVoZcFRIEeuoUMOMWc7jdOsKWGRfH1YMw6hfDydQa58cNpVSrnetdwwSOcYJ0te8LY2xlcHwmR2lpGsJeeUYqRPKPHSJJMvqdRVIhcu6Nf9Ndcg2XYqPfe91glzi/9KXqyDC6nZjU/yJ8h0AievzAQqWWxhf3B8fqELFs3YkGk94ceYoTZLrzYvb8MxWO36KxKV4sKwuoxmfP2qzJuAJbbJmMB2C9oQYlSlqdueVb0qp1sB/FWIEwgWOFxnUN8AywGDYxy5vXmJQA7AnT/oppNJk/7ZIKEa+7/jZQT8OwUOLvfeaFJwxAnLj+g/Zs1iyDc6VLBRLR6uCvxs6rpZKsajkilv1BnN3Rb/GDwFJbdst7FbiD1WT/TEV5u4NDYSjJjBgc1O/g1XNFLYKlpcFbODLqmtLDG1UidCnJrzn8pzqrHmSooB1eZTRytg6ZwHFJq4vukjPGYs8UeaU5e0XMqiVSIZLxw9ba2PMQUqsvhFIRvaKjLevnXT4P6HMjnbj35mt7vxtqNU3tTKUrK3UKW0v/uvV+gUR0JAXSZVipO3LplX0gZnpXqKNjpkdGMHyE4mRLP+YA+jf/TEV2g8YBFDDDT0bvfywxEezgcWZAGGHL5NISQu+ZLdfGcXJQRSRXOcKanF6rUWgIisEHlofM2hXacIxOFCZpE+yOKlKZzEpXcQGE4I7cLMgpnPHDNpuC78jPvrzxY4M6HPzcjOKTHjZbMa5wSnYWGpGv1mRS7CQqtx8Ry6asoe25TLF/utUwnKzrxsLzONkZhkCswexJlUX3gejVB8Q97u5ad3eihwOXcANb5rFjLLJl0ntCRrUxnOxT4PGcEVykVKvlasK19g9IkjYcYzhpAmQT5tQEjq9sXisVIpe/+ChSvFAqRC4sW2B9XdXGnIsUv2wA4qi3X6u7GGm9CStXN5/fLZCIHt3/0qCaKhPjrG2liFjmfrGTlQGw1UnXEJ5VrzmXzwIuB8kUV6vUdd1gv87VXjkTcaHnzwPVOC3NpNz+U8CWGRICTNJ+fo4nsLd//JAaUKEhqjrRmGKVd5q9QQknMxQJZer6HkBWg7oEVZSpkGjDceNEcTce+yvSbMTmpWhvhcCa+MVqqRA5ce/NQ82NpsLTnRMYVnjCHWaPBk1u/MOVzWu7CvPMVuao8N9HxQKJaFXgRor9+17pQsSyB7/icBOf4khMqmlQ/UoYpQAAIABJREFUQqUFf8vb0UvlarrMBhH5yuwGbdcQjhOk2c0Ak9s5/RSvqACq8YkTpIbl3LJEa6vW1xdg8ZkzBMdBJQ4QI4qTOEG29mMp1Rq62b+Cs5VpNZpOXfyti/8204NjLUYkVzrCpjMWKx1Qklw5QmFx7cfvQHaP4V242phzY9fcYFNDhts277uFELhPPjRV9ssPym4nqJyl7ZU3fvuQQCI6lOQ1dpxmS+74vBARy0Kv9Zq96vRCFCcgnmoxok+JN/VhFR1oXpM2tUYTX6qOLlJdqVBn1mmKW7S1XWjHIK7QEDhBajAA5U4fPPUBoAEBwLmtoIB6Eyo1HcOWSWUkrNdBcZB5D8NJhYboGsLre7CiFjSjVpNYro4sVCWWqzNqNUUtaEMP1i0HqwLDQaSJJb8p1odnZ4f04FiDEWHc2Pisom33rj07Zk2bgiDIpGnzn9uYeIgbdqGwPCVkmCYwzFt0I/CpuGOSVIhk7tthIuW2rLSEdSsNdonQ+U+WBvqa1HHwaVDOOWhEvlxF6bP3p/NtiFj2/G67Agsd9oxQ25WrQSCJQgMwV67RH7vsBp1N4eA5OUB7DQmxWZNWBUeyZdIaGHeVMd0vsQYlxhHympUGPTjGcNLf0ZlJu7evmY0gU+5b7r7+cPTajaumIcik+YH7OUBk/0wFNB8Xeh2B8XJSIRK14lVjwVWdO3PujX8bgDj2/Tcbr8QbV3DisSRyr0Aimv7T8z2KPpvDGFRi//teDiKWJZc6IWba5vAmfgWFQuvhAVTjGhp+kzbFAv2XgTE6XZ+O0WYTvoLrSIAeHJOkw/fxgtwfRJDb3k+UDuPv/p2LEGTKYg977fpm9XE4MTAntFSI+E7/OySbR5WK/GMHT8++FwLxsb//6erWDT3lJa4zkXAkCzzeEUhE7/iPpCaxMsLN/iBm+q3DbMKBldvxl4wlAJktseho40I7j53FlmnnsPnmBgnQg2MN6ugAkEP7Ntw3bf7KICPwDfeZgSAzdtaYxVM7CzUoURURYlB+6+Oj+moqU7/b7PlPASw89dhduUd+1gwOGCToUgeVnbW37JghkIh+vnzM5sCq29UwjV5pM424Ppvd8hVsSoBob9c7t3WwlBDAqWyZNp+Xr0BRAvTgeICL5A46/XeU/QGWrIg2aMSjQNZjxyQEeW5fy6jCYd3ZzsIBFX7mxVkQeRPXf3jxo7cM0Bz+2rOQRYiiZM1UGyE4Hs7VNLbETDN6RaH5UdCIHF+ebLPlSvdaRCxb7+vMmGmbg5x4FdCoKGBPSLY9QVSenZDLIRWc1tubaDTv/0OlH76O0yVAD45bBzgh1dyzHdgfZu3WKbyJaYunI8j0HZJ4I41Yj7bdh3x8ntNd3Z449ioLJQUXL0H89bp7sgGIL655uyXN/jdnJIEpIKcfndJ0uISd9fBt9E8Ciejevc92DHVb7zGjEsRM/3FFdveQk2OmrY9zIl0Fbg/u7lpPT1LJApET0dWlZ8sMCCC6aCS0nUginTDPQg+OKzs4Co8uXj1/CjJ5zfqobsnq2Qgye7FXt6me6wWUYvDn9lVrQ1lAXtP+dYh/XDTFgMLH75iUtvPr/lq6dJQgM1NAE2mcQHo4t7R+2ZickiQ5tsTOFfaq1yqBRCT2+9RmPwv2ViJi2Q9hrTZr8hVYkQAaHAx28HJz7e8NsGV6eQH3jIgI0mXYMu1/ruu2B3pwnN3AGeVSqM+sycikmXOnIciDW4rNYGVQ4trD0et37p0zcwoyee5ijzF4zYa9Is7zFITjsFee6cjLYrYshrE13W23m9toLZjUWyeGjRUkaaaE2V1Ht6rtaZy66wmBRLQ34cjoK6Zn57P7ELHs72tZdn01vQ1/rpMAnp8P0DPQWsIXiqLCy8uhARqQXbgEAQ7FgfPVLEqAHhxzph0DbVfnMoEgz7rvsQ6s8YkL70GQe/Zut16N0dXUwLPBcx81KMjR7y6qT6C9961PDp3htiQwPWB3QBuZ7oYY4a8unylis8TilFG9EFEYB43IMaU2srXP/AbETHsmOCGAherDTIx6arXW2xuoxpWVdj7QCFsmSwZoO8fDN2dFAvTgmCPbsU4Xbln/Ogj0QJDZS31s2CK2r38EQRatDrdRzYyKbQujWwdAKHtHblbiBhAbDf+GPP9YyanjNMTdFLBkWUB6oFtAE5m+e0lAoJvOUmzUAdCRoUFjuHBsyfAVe/7fFXdAIBHd5fZ0S3+7lX68EkHM9KNbXM5vz8qYx+MlLDUVqMYXLtg5+HHBlmnnM16fzenB8YAKZ4BxVJoc2rdmEjJl1tZA3T7eiOa7fePcWyevWR87Cnm3r5mCIMvXnh9VSOUuNusMqEbyFcrbWzP37Thxn96a7HP/LZn7dsrbqdhYgfl4yTKdRpzhtmTZkiWBbTpTsh6CgfoM8ky3BSwzKWF/Eb5+YrVAIlri87H1rm9bV4CIZeFZtuNHrPfDX7UkAaK7W+/c1kplCVnqhsQuXYL9cJo3xOLt+QtcSoAeHHPldxweCAzHulg7qdeOaQgybU0a9HKTHt4yCUFmbDeyJuuszMiTtmwathRhs9BsNmVhsd/xkOdnGpTlxC9Wd+TaMCsDTwkAuNA0PKwIj3VrG1vC9mQ39bXe+cOTAolo90WQ0MTSn70RrYhY9qLb+IiZtvQUrlyOxcYC57bLl5kPUqNBL1wAWHzsGLuxfMyHxLdkVQL04JibqDzoVrF8rd74MOxcod+sq1m78A4EmfLgez4gSHrrhhlTEWTy8tXcOFdYkW19QnSULjkTxOULyxbUREdYqe86lyJLEqAR+XyxxXjuPjn2P++AmOnUcqosna7zgK4/EryuDsDo0aPEEEPxjrBlnjw5TtkyXX+anD5CenCMcsBZsWfncmCmgE7HUKWFm3Ujrsctu3ZueFBHIYRMnT1jhc+u0bYLs3oug0L/TIVN/r3u4oKkrz91v+k/ICgHPvNgoZcUR13dadct/rBAIrr9+9kNvRYzkmz0a0TEsneO1Dp9UU68AaChoWAHTyZj9mhEayt68iSwO08ItkxmQrgeWtGDYw3qFEY39m3EZsEaMLqhlOgZVb3d2Qf3nHz4nxCUve76W/rubwYb6lx5xSz1/UQgES3y/sDSICtaVTBmurLVtWKmMZxQagg55HJDAWUiQZBajBwvDF6QYg3197ckeevleE2N9uhRgMWRkazTIlu/NX/VwRKgB8cYTjomh5VZuOS6MLlKbVM7NpmesqCTZxc8ZTArJ3z6Xuu1VJM6LnLaNtBxz55nBBLRjtj9lob09m8gZnqTn/MDbVVawFE7qCIae7HiFm1mneZKBaA5Ds1R+mUovFPlgTLFuQJlfKk6tUZT0qpt7sP6lThBAJJy1/qDopAGHi8rYzCw65Atk4GUJkwTenBMkoCQn2tYdFb/Lf0ME7Y0JV+K/WCpAZQjFs+righ2wSUSW3YFGpHDCmLMDi+lfAgRy/7n3Zw+BUNRmO2WeiHImqNLnZdcqT6dpWCwEmKKVdVdqFKrU6Kp35izmlhGBlBsI5hsM/BsmZxNi4t2TBuOcYL0SnWQ9YDB28i4iVeq3E7FqreyLOXbjcf+8WeIy6dnifLcD2gVcpea+X2X3AUS0d93PlbdbT6H/ItuFYhYtjcCeIU47A9OkHINUdoGUqIdZ2l1ReQrM+s13XLcJAWiwx6KJEmir0/v3NYEouZp/cGSk/UObXkOTfRFa5B8ZXYlQBuOUZyMKVYxRj2XbRhTrGIlm6FWPpQr3e//+N0QlD1u+7+U7V/2VbmQA9mKU+sEEtHC4++ZXUnhmSBm+rZ1jouZ1mJEZr2Gu9/4hDJ1n8I5oIzFxwPntoQEs6K2WEgQ0CsO7P6Vl1usxl+YcBKgDcckSXIaKu0svK7oYNk7ojIsKHzRcwYLRtzq5U1XbQQrO2Z1dcl77v/xOYFEtDVqn9k7PrKlBBHLvC87gh6sqEUbwMgoQXedJFepFRrCkdQORGOjXjXuoxFcw7Nlml2T10khEzhWahxNQk/33WNQX6HhZA+oJeNq/CfvGED57MtzykNOOX1tXapIgUbkkDwzAbseCZ2IWPbYN6XcjZMgyLYBPDxfyWCmGDfxTpPnNGpZ+QaiIhnIQYxlZFCpDOsAtswAkMwU5dkyqUttAtVkAscYTkY49kVi/AZSbBiRr+TUwjhQX5v+/Zbjd/4F4rLfo7dnH9qr7utx4kL65YqHQCK6+btHyjvMMIje+kk+IpZdyKah1lF/FhQjL5U7LR/5qWuKln6MazUZLy0FqrGvL4lR3RQF2jRkywwPJ+SuteVAfXL5mvZIgAkckyTZ0DOh/Csaeqi+M/bIGtdqCo7/FjhnOgRl9yl/TN7yWXdJoT192tP2vdMbBBLRS8dWjO1kdxiImX75R3uJx0x6JgiyT4mfyXGoUmz2JzmnkZuvIfjABIGeOgUsv8XFJhKwdMqzZVqSzHVVzhCOcYKMLJwgG3qRhSo7fSrorpiayLDzS18yWDCi33u94VIs3U7sr9+nHHjo5xcEEtHXF9xMeusaRP+4IhsRyzIqWVPTUJys7caOM+ISMQupdhbGFHMV7QId1NCzZ02kaukUz8uDVma20jVZuhFf7uISYAjHINcFN4ma7HzHGDRv05FqOn6e2nMyE9d/aADlM/9+vMTfy8HDSKrOgEbkgOxwk1uv92lAxLKV7qzFTGfVaxjMDqdNgmS2w+JNxGLzlBgchEF0RL15V0KTHkbYMrNs8FKZNORPJ54EmMMxhpMXS51mAWTrLb1YqubUamxzxchbm6/9+N2Je2+GuOzzwK2ZP+1SdDjO7ffXZG+BRDR52/TitlHeeKXN+pjpmg61zaewWSGhzHWXSp8Sx9jbx8USE4FzW1ycTZmQJM+WSUVI11Ed5nBMkmSPgiv6Y7bQ1mY/PYoRgmPnTnvxSY/geTMMyvLljR915DFknKH7IB8EfSmQiOa5LzdpKP61BhHLvvKnHcJg0s8lF8ZiuEJUWnbwmGhp0Tu3ddtIGktqNGhkJKjMs2WaLJfr+NQuOEZxMqnSdbUem1icVEmbpAIsFYskxeluCOJGw6/JzLqruxgV9fZrBlC+IH65NuacmXqsFsk1ipm/zBdIRJsidhl3nFQyiIhl/7cyd8iIld+4gs1jLUaUtHKWX5E9M7RXqpwVXwv0/HmgGqfa4C0h+vvRkBDg0HbyJGEfG73NKeArjCMJ2AXHJAliWwNlTLgFbGIl1xUCZQo5k931toBleswFHPMgM6n+Dzi1G45hX11FeVc2r5Xe+AeIy0H/erjQ252g7DI1PCIa/6fWZkEj8smsM8bN5u0GMdM/X2BiPEExsqlv3HjgRBXZu7OHV1QAbdfb2zrv2ghbZkgIQSdCxHhe+OMJKQF74RgnyM6hcWmy6BrCrTpUgARLAeAzfRT+6tIs6VfCcMZo3WmGG7I7wIDUrKwVVXeX7MAPJx+6DYKy192TM37YOthEaYOIwQDcU04KJKIbvrk3v2Ukad6ZjF5ELLvjcybeeIOqcRYulFmnsbokbAgVRnDgBdbiywFb5rFjPFumDVFer5fthWOSJLUYUduNcq3Mstt/dReqtbV9M4y26W673dyAFjyCy8P2CkN+aJgr2qgCq+upLNA3dP6TBgtGwrqVbZlprN5B39nHIVsEEtG/flti3PmDXxUjYplvki1jqHEbkkQxMizP+f7FdJdNJdNYeTwnB4BsSMhoMYw6w4uLoWUZu3Rp1AX+hJeATgIswDHYIMbJguZxYCKEL2dBs5aKNwVIMLo7ncxwWxKYHrA7oI2EmGu0cDKArTidJNN3Q/MFV3AMb9mYFB/7/psGUD73xr+rzll7+Y0GSvVQjWpmHXxFIBF9Hrbd0Mb9Ygcils3aRi9mOq5kvLqldw3R391VKLSeniDuo6bGIDeTAzwrS4/FaZz8lJrcjj8djxJgB47hk4+LNzCuhLKJsClgybKA9EC3gCYyffeSgEA3Y0sxeGSwp7ckoAns4Bn/sXM3z/oy6q0ovbr1i2N//xPE5dOz7807ehBVKqy3on71Wn0uNCJ7XwuCrQiCvPljEDMdndtPsZ/arnFjMh6rPp/NVdLd1oNkmFh0tCX58GyZliTDlxtLgE04JknSN8Olt/V8M2jBFjAfL1mms0hkuC1ZtkRnOG4LWKbPDA3UZ5guWi9RbrVj42nTDA7kHvnZ//G7ICh7/lOQ+t3mvmp2Ypo9009DRJY16s2gO0NbELHs1Z+qjMdg6RjDyeDs8WemMMZlWvR+REcHVHuJjg4zMiEILC4OVuDZMs3Ihy8ykgDLcEySZJCrOloEZNHCYiAk4CwBAVevCOskZ9HRzXFwbJjBirMB4a/NNVgw4j4SN6fYkTp+uN9PQ7cKJKKnfl2E4oB3tL1fC9PoyaptxExjOJnbOG7MVsYQbHwckKWgTvyGRkUB57bk5GHhGf2vUEBeN62XF9HQYHSBP+QlYEYC7MMxSZIXS13ObhhttxuTGeG5TFFLenL8mrcNoBy28JnyMwwTZcJnwnDs6cOLBRLR2tBvYMmn3iBm+sNjNtKzqlHi1DWX/kIyhl0rx1n1lLws8OpqoPl6epJKpcly4NkyTQTCn9qUACdw7GoqUlqNhm5OUpuCc8EK/XXVabu+Pn7HJIjLfjPuzPl1n7q/l9lQc5oKocnCIw0ge2GjEirI9V0aSx1qMTKj1uWIKaxgrpVLvhkKJYVQPTQ4GOzg5eSYyASwZXp7A18Lni3TRDT8qWUJcALHJEmiOEhy6p3m5Kx6Xqnyyg6UrRBYy2J0oSuYWl3geTjg6QcgKB+95b+SJZ/3lBYxGOKJa8EQkTPqAdwsPViNiGWSgGZLXQ2pCafPuBWEpXsptVpj3QMHz88HgBsQYCIQfTyIuzsWG0vi9P00TLrjT68bCXAFx9D7rV+JO9H5NCxP2S3HNSg7dATjbklUXzh7/s0XDRaMmJVvNCRS4rUxftIN4d8JJKLHDy5UadWJRSBmetL7uUqNGYhBcTKtZoKoxgbgVltZPGo11H/xylE7qCNsmUlJxpLkj3kJ2JQAh3AM763SEuXtqH+mQ+2J/pmKsjaQ3d3m80/4Cu3ZGZc+/8AAymdeeKLktDetp372yJsCieij4K9Jknx2Vzkilh2Iah/bA06QrsArb0BSVg5quixmUMRSU4FqfGFUdissPV3vXMyzZY5dInyJLQlwDsc6NZnQoISsQeMA6vHjKXJZg0aDEZg94a62pDburg+1NF3bu/2E6CaIy77T/5718/eKTjOoOvbRClpK/7L1PoFEdCTFNyitBxHL7t5gxvQxAej9xiJ4Qpl5/lWiu1vv3GZEAASpNYEpuciMfMYKli/hJWAiAUfAMbylBiOH1MSVCg4Z4K5UqIfUhNYRiZZMxDhOTgmiyPdY8HOPGpTly5vWdOZn2xy9nywUGpFTajLv2wRipv2vjoqZVqNEdsO4928bC8cnLTiqY7GxwLnt8rBPoYEt8+hRvNpM7kGbEuYr8BIgSdJxcAzFrdISfQq8oFl7jr3kp+fylUUt2j4lfj24T7CyauviIqNWvGoA5ci3FtbGnrfe85fnvhdIRI/uf+nnqDpELHvquzLj+mqUOF/gct6NY+GVQUm33NRQTtTVAdX46FFicJAkScCWeeYMMFzwbJnGa4I/pi8BR8OxYYQqLaFCidpuLKFM7ZNO27Lsk65IKFPXdWMaFPRj6JY/oC6BzsLcK19+YgDloGcfKfI5Slj2BHjeXSyQiFYGbBSuzkPEsrj8AcO9tNg4I2+jjsvZDaaOfWhoKLBI6KzDRFsbevIkwGKeLdOwGvgDphJwGhwbBgw3r7uG8PoerLBFm1GrSSxXRxaqgrOVPukKn3RFcLYyslCVWK7OqNUUtmjrezBI8nLdukwYRMfKgbKrQ/bLbt+HpkJc9r7nxgy3bUPNZkLIStorhd8+KJCIFhw+gIhli/ePxEzXdI0zSj/qcBycrTT+tceLigD4njpFkuQIW+aFC6SahRRWrEwo38n4lYDz4dhEdhgOGDs1KKHFCJwgcWLk1LoTqEk//CldCZQG+IS+NNugLF/6bFVb1gi5PuwtMCcCGpH/c5UvIpbl1oG4cw1GTJjoD7MwPbIrjKJaX1+gGpeV8WyZdBcYX9+mBFwOjm2OmK/AqQQaL1+MWbXEAMrnlrxQdT7U+I5bLuwRSES3bH/2DyuSPvYEXPhyDZFYzvoObeJiBEHm+eynloFpz5b5CLJodTgnYUcGaxiWkQFU44gIXCbTO7TxbJnGi4M/tk8CPBzbJ78J2rqnvOTq1g3Hbv0fiMsBT96X73EIVelpGeZ7vC2QiP60/l1ELGvu0cg1RGQh6/t4dODYZ+80wHDKFRz36vLbEn19egiOiYEHeG7uBJ1//rGcIwEejp0j93FxV81Af85vP516bBoEZc/bb0jd8VV/TVVFZ82U7x4VSET/vWb7t8HNKi3BAaMmdThOWzodmTR5Cndw3NgLfCexhARoNdZjcdko35JxMaH8IF1cAjwcu/gEucTwKkJPh7/6rMGCcfHjFadP7oVG5L+u9VVqCAa+MWattEaFFOG4e/ua2cj0HWu3LOIOjguatURTE0Rh8C/PlukSq3ICDoKH4wk4qRw9UnNq0sWPVxhA+eDTdz7z5i3/t/mpH86VG8EoW9ZbPRzv8vN5cc69kxAEmTp7zrro/Umj+pd67JiGzF7qI9+/k0M4zqjVoCEhEI7R06eJzk6OJMx3e51LgIfj63wB0H78/tqqtJ1fed6up/Hcfcd/LV4wgys4njxl0uS5czYGbjoctnL53EkIMum9aKlhcy8+cfF0ZNr6NM8UbuG4NjJZj8VhYcTQEG2R8Q14CVCTAA/H1OTE1xotAVSlyvf49eSsu6VC5PNvvvEwQCRrBzrtGJm/MsigDresXYggyA6J/hbdkvcfQabv3a475VQ7vnKtTevrC7JEW46RGS0e/oyXABMJ8HDMRGp8G4MErvpLh9RchOTp4PhZnz1G+L5n+4hFQnpwwyQjsOYUjhPL1WoF5Yy3BtHwB7wEaEqAh2OaAuOrj5EAipNcGStG+x2PYG5U2HOTkQe3FBvuO3LJCL4NV+08yKjVGFyPxzw9X8BLgDUJ8HDMmiiv244wnHSMZ4UBc3UHwNPY3B+DNcNg5bD3oKBZq8GMI6Wv26nmH5xbCfBwzK18r4feHeZ3bIBjaXjapsPRxn/Xvj8XQeYu3B296XDOIbYV5KpOlODR+HpYys5+Rh6OnT0D4//+crWDovIMcDzW+GDl0tjKdEvaB005Nsf/pPFP4IoS4OHYFWdlfI3JYZwVVjDXyiW64Du2/pCa143H15Icr6Pl4Xi8zpzrjFuDcsHoZiYqzwrmWrk0Fl7plvBUgq6z2Cb2SHg4ntjz66Cnm8B8x0EyJQ/HDlpG1/1teDi+7pcAGwLQoFy4HtvrEUFXCzZbP6UaZMJlQ0h8H7wEbEiAh2MbAuIvU5GAGiVYTH5oFhadVdjUx6fCpbIE+DosSICHYxaEyHehmqCZpL3T5COpQPhp5iXAsQR4OOZYwNdN991y3FkKLHf3jSlWwVyO18008g/qTAnwcOxM6U+ke+MEGZBFOyM4d0jKSs9FLdqJNEf8s7i4BHg4dvEJGjfD02JEchXrGfOcvJsn5z2Ox80CnAgD5eF4IsyiizxD18SyV8SXqXnDsYssretkGDwcXycT7YjHxHDySsXEUZD7lXxstCOWDX8PgwR4ODaIgj9gQQJDasI7zckWBlasxqnVGj76g4UFwXdBRwI8HNORFl/XlgQwnMys07ACiE7sxO+agneosDXV/HX2JcDDMfsyvc571GJkoGx8u1iUtKLX+STyj+8UCfBw7BSxT/CbVneiTtRt7bx1aI6S38Gb4AvUVR+Ph2NXnZlxPq64EpWdsOis5h08u/E4X3vjd/g8HI/fuXPpkWsxMixP6SxIZXzf8nY+8YdLr6uJPTgejif2/Drz6QZV44zmLbOO96Zw5oLh783DMb8GuJKAFiOb+jDGiqqDG0YVqbgSBN8vLwFqEuDhmJqc+FqMJKDBiJJWrYOBlcHtvFLlfHJSRjPMN2JTAjwcsylNvi+zErhU5uqheiotj8Zmp44vdKgEeDh2qLiv25sluDAi9ylxPgDvul2ZLvXgPBy71HRM5MFk1btctF6QTIHiJO9lPJGX3bh6Nh6Ox9V0jefBojhZ3o4eT3EVRouYYn7vbjyvp4k4dh6OJ+Ksuuoz4QTZq8DP5DjfHzmrXsOnI3XVZXL9jouH4+t37p315FqMiC912ubeqWuKpj6MN1A4a/b5+1qRAA/HVoTDX+JKAjhBtvZj4fkOVZO90+QZtRotnxiaq1nl+7VXAjwc2ytBvr09Eiho1jomw15ylVquJnil2J7J4ttyLQEejrmWMN+/NQkQBKnBiGt1Gq9Urrb4EsrU3XJci/G2YmsTwV9zBQnwcOwKs3C9jwHDSbmaKG5FY4pVx1nC5Yh8ZWadpnMIR/kUS9f7+ho3z8/D8biZquthoGqUQHGythtNrlSfzmLCYR9TrCptQwdVoB8NymvE18OqmTjPyMPxxJnLCfMkBEEqNMDOO6Ai6nuw4hZtZp3mSoU6ukgVmqP0y1B4p8oDZYpzBcr4UnVqjSavSVvRgbYP4jhBqlBCw6fymDBL4Tp7EB6Or7MJH4ePq8EIuYYYVBFyNaHSArUXWJxRQqEhhtTgr1JL8JQT43Bi+SGbSoCHY1OJ8Oe8BHgJ8BJwigR4OHaK2Pmb8hLgJcBLwFQCPBybSoQ/5yXAS4CXgFMkwMOxU8TO35SXAC8BXgKmEuDh2FQi/DkvAV4CvAScIgEejp0idv6mvAR4CfASMJUAD8emEuHPeQnwEvj/durQBgAAAGHY/1/jeWCmEkVSMQKJgBwn7E4JECDwAnL8IjYBAgQSATlO2J0SIEDgBeT4RWwCBAgkAnKcsDvJQx7hAAAACklEQVQlQIDACwxBC3VypIheaAAAAABJRU5ErkJggg==" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's go deeper in the machine learning by talking about neural networks, \n", + "Neural networks are just a bunch of layer filled of neurons that are linked together.\n", + "\n", + "There are using the same approach of the machine learning like you do before but here you will use matrices to process multiples calculs.\n", + "\n", + "did you remember the following formula for process multiple neurons ? \n", + "\n", + "$$ y = W_1 \\cdot x_1 + W_2 \\cdot x_2 + b $$\n", + "\n", + "it is what are performs in neural network but with a lot of neurons !\n", + "\n", + "![matrix_multiplication.png](attachment:matrix_multiplication.png)\n", + "\n", + "This notebook is axed especially in the maths behind the neural network so good luck to everyone !\n" + ] + }, + { + "attachments": { + "y1_formula.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 1 Be a neural network...\n", + "\n", + "for this first task you will do the same work as a neural network will do to learn from an output. *(we remove the bias to simplify your calculation :)*)\n", + "\n", + "imagine you have 4 neurons in input and 4 other neurons in output, these neurons can be see as two layers of each 4 neurons, these layers can be represented as two numpy matrices with shape of (4,1).\n", + "\n", + "In a neural network all the neurons of a layers are connected to the other like you already know\n", + "\n", + "if you have to predict the value of a neuron called $y1$ it will be : \n", + "\n", + "![y_prediction.png](attachment:y_prediction.png)\n", + "\n", + "your goal here is to create to matrices each with 4 neurons with respective values : \n", + "\n", + "- [0, 1, 0, 1]\n", + "- [1, 0, 1, 0]\n", + "\n", + "and to calcul with your *intelligence* all the respective weight to get the second matrices.\n", + "\n", + "we can set the representation like this : \n", + "\n", + "\n", + "$$\n", + "y1 = \\begin{pmatrix} 0_{a} & 1_{b} & 0_{c} & 1_{d} \\end{pmatrix} \\times \\begin{pmatrix}\n", + "w_{a1} & w_{a2} & w_{a3} & w_{a4} \\\\\n", + "w_{b1} & w_{b2} & w_{b3} & w_{b4} \\\\\n", + "w_{c1} & w_{c2} & w_{c3} & w_{c4} \\\\\n", + "w_{d1} & w_{d2} & w_{d3} & w_{d4}\n", + "\\end{pmatrix} = \\begin{pmatrix} 1 & 0 & 1 & 0 \\end{pmatrix}\n", + "$$\n", + "\n", + "Don't hesitate to check the [NumPy Documentation](https://numpy.org/doc/stable/) if needed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create the two matrix\n", + "N_neurons = ...\n", + "Y_neurons = ...\n", + "\n", + "\n", + "# TODO: Complete the matrix W\n", + "W_weight = ...\n", + "\n", + "Y_pred = N_neurons @ W_weight # the @ operator is used for matrix multiplication\n", + "\n", + "print(\"Result after multiplication :\")\n", + "print(Y_pred ,\"vs\", Y_neurons)\n", + "\n", + "assert np.array_equal(Y_pred, Y_neurons), \"Output matrices doesn't not correspond to what we want\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What you just did here is exactly what a model does during the backpropagation pass (you will see after)! The model repeatedly adjusts its weights to better fit the data, just as you did. Keep in mind that this is how the model updates his parameters to learn over time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "___\n", + "## Step 2 concept \n", + "\n", + "## 1. Data Preparation\n", + "\n", + "**Dataset Overview**\n", + "\n", + "*The Iris dataset is a classic in machine learning—like the “Hello, World!” for ML.\n", + "It has 150 samples of flowers, each sample with 4 features:*\n", + "\n", + "- Sepal length\n", + "- Sepal width\n", + "- Petal length\n", + "- Petal width\n", + "\n", + "*There are 3 classes (species of Iris):*\n", + "\n", + "- Iris Setosa (Class 0)\n", + "- Iris Versicolor (Class 1)\n", + "- Iris Virginica (Class 2)\n", + "\n", + "\n", + "### 1.1 Import the data \n", + "\n", + "You need to import the data in two different dataset call train and test, respectivly for training the model and testing it with data that he never seen.\n", + "\n", + "### 1.2 Normalization\n", + "\n", + "Each feature (sepal length, sepal width, etc.) can range over different numeric values. For example, sepal length can vary between roughly 1 cm and 8 cm. To make the training more stable (and keep our brain from exploding with large numbers!), we often normalize or standardize these features:\n", + "\n", + "An simple demo, if we have a sepal lenght equal to 4 and all the value are between 1 and 8 if we normalise into the range $[0,1]$. Think of it like converting percentages, it becomes about 0.5 (or 50%)\n", + "\n", + "Normalization typically scales each feature into something like [0,1].\n", + "Standardization transforms each feature to have mean 0 and standard deviation 1.\n", + "\n", + "For simplicity, let’s do a quick normalization to [0, 1] by dividing by the maximum value (or you can also do min-max). That makes everything neat and tidy for the network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 1) Load the dataset\n", + "iris = load_iris()\n", + "X = iris.data # shape (150, 4)\n", + "y = iris.target # shape (150,)\n", + "\n", + "# TODO: 2) Normalize (simple min-max, for example)\n", + "X_min = ...\n", + "X_max = ...\n", + "X_norm = ... # scale each column to [0, 1]\n", + "\n", + "# 3) Split into train/test for a bit of realism\n", + "train_inputs, test_inputs, train_results, test_results = train_test_split(X_norm, y, test_size=0.2, random_state=42)\n", + "\n", + "# TODO: 4) Print the shapes\n", + "...\n", + "\n", + "# TODO: print an example\n", + "print(\"-\"*40)\n", + "...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Model Architecture\n", + "\n", + "### 2.1 The Building Blocks\n", + "\n", + "A neural network is made of layers. Each layer performs a simple math operation on its inputs.\n", + "\n", + "### Layers and Their Components\n", + "\n", + "1. **Input Layer:**\n", + " - **What it does:** Simply receives the data.\n", + " - **Our case:** we have 4 features as input\n", + "2. **Hidden Layer (or layers):**\n", + " - **Layer size :** Let’s pick, for example, 8 neurons in one hidden layer—just because we can.\n", + " - **What it does:** Performs a mathematical transformation using *weights* and *biases*.\n", + " - **Weights (W):** Numbers that multiply each input. Think of them as “importance factors.”\n", + " - **Biases (b):** Numbers that get added to the result, helping the network adjust its output.\n", + "3. **Output Layer:**\n", + " - **What it does:** Produces a score for each possibilties (3). Then, we use the softmax function to turn these scores into probabilities.\n", + "\n", + "### A Simple Example\n", + "\n", + "Imagine you have an input vector XX with 2 numbers:\n", + "\n", + "$$X = \\begin{bmatrix} 2 \\\\ 3 \\end{bmatrix}$$\n", + "\n", + "And you have one neuron (a simple unit) that computes an output using:\n", + "\n", + "$$Z=W⋅X+b$$\n", + "\n", + "If W is $[0.1,0.2]$ and $b=0.5$, then the calculation is:\n", + "\n", + "$$Z = 0.1 \\times 2 + 0.2 \\times 3 + 0.5 = 0.2 + 0.6 + 0.5 = 1.3$$\n", + "\n", + "This is the basic math performed in each neuron.\n", + "\n", + "### 2.2 Our Network Design\n", + "\n", + "We will build a network with:\n", + "\n", + "- **Hidden Layer:** 8 neurons with ReLU activation.\n", + "- **Output Layer:** 3 neurons with softmax activation.\n", + "\n", + "You might wonder, “Why 8 in the hidden layer?” Because 7 would be too few, and 9 might make the universe collapse (just kidding!). There’s no magical reason—just a decent small size for a simple dataset. *But think that use mutliple of two between layer make the calcul more efficient*.\n", + "\n", + "\n", + "\n", + "### Parameter Initialization\n", + "\n", + "We start by randomly initializing the weights (small random numbers) and setting the biases to zero." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42) # Set a random seed for reproducibility\n", + "\n", + "# TODO: Define dimensions\n", + "input_dim = ... # 4 for the Iris dataset\n", + "hidden_dim = ... # Number of neurons in the hidden layer\n", + "output_dim = ... # 3 classes in the output layer\n", + "\n", + "# TODO: Initialize weights and biases\n", + "W1 = np.random.rand(...) # Shape: (hidden_dim, input_dim)\n", + "b1 = np.ones(...) # Shape: (hidden_dim, 1)\n", + "\n", + "W2 = ... # Shape: (output_dim, hidden_dim)\n", + "b2 = ... # Shape: (output_dim, 1)\n", + "\n", + "assert W1.sum() < 13.94 and W1.sum() > 13.93 , \"W1 not well initialized\" \n", + "assert b1.sum() == 8 , \"b1 not well initialized\"\n", + "assert W2.sum() < 13.46 and W2.sum() > 13.45 , \"W2 not well initialized\"\n", + "assert b2.sum() == 3 , \"b2 not well initialized\"\n", + "\n", + "# TODO: Print the shapes and the matrices of W1, b1, W2, and b2 to ensure they match the expected dimensions.\n", + "..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Forward Pass\n", + "\n", + "### 3.1 What Is a Forward Pass?\n", + "\n", + "A forward pass means sending the input through the network to get an output. It consists of:\n", + "\n", + "1. **Calculating the linear transformation:**\n", + "\n", + " $$Z = W \\cdot X + b$$\n", + "\n", + "2. **Applying an activation function:**\n", + " - **ReLU:** Sets negative numbers to 0.\n", + " - **Softmax:** Converts raw scores into probabilities.\n", + "\n", + "### 3.2 Detailed Steps and Examples\n", + "\n", + "### Hidden Layer Computation\n", + "\n", + "1. **Linear Transformation:**\n", + "\n", + " For the hidden layer, we compute:\n", + "\n", + " $$Z^{(1)} = W^{(1)} \\cdot X + b^{(1)}$$\n", + "\n", + " Here, $X$ is the 4×1 vector, $W^{(1)}$ is 4×8, and $b^{(1)}$ is 8×1.\n", + "\n", + " The result $Z^{(1)}$ is a 8×1 vector (one value per neuron).\n", + "\n", + "2. **ReLU Activation:**\n", + "\n", + " ReLU is defined as:\n", + "\n", + " $$\\text{ReLU}(z) = \\max(0, z)$$\n", + "\n", + " For each number in $Z^{(1)}$, if it’s negative, we set it to 0; if it’s positive, we leave it as is.\n", + "\n", + "\n", + "### Output Layer Computation\n", + "\n", + "1. **Linear Transformation:**\n", + "\n", + " Next, for the output layer:\n", + "\n", + " $Z^{(2)} = W^{(2)} \\cdot A^{(1)} + b^{(2)}$\n", + "\n", + " where $A^{(1)}$ is the output of the hidden layer (after applying ReLU).\n", + "\n", + "2. **Softmax Activation:**\n", + "\n", + " Softmax turns the 3 scores in $Z^{(2)}$ into probabilities:\n", + "\n", + " $Y\\hat{Y}_j = \\frac{e^{Z^{(2)}_j}}{\\sum_{k=1}^{3} e^{Z^{(2)}_k}}$\n", + "\n", + " This ensures that all 3 probabilities add up to 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: implement the relu function\n", + "def relu(z):\n", + " ...\n", + "\n", + "#TODO: implement the softmax function\n", + "def softmax(z):\n", + " ...\n", + "\n", + "def forward_pass(X, W1, b1, W2, b2):\n", + " \"\"\"\n", + " Perform the forward pass through the network.\n", + " Input:\n", + " - X: A column vector of shape (4, 1)\n", + " Returns:\n", + " - A1: Activations from the hidden layer.\n", + " - A2: Output probabilities from the output layer.\n", + " \"\"\"\n", + " X = X.reshape(-1, 1) # Reshape to a column vector if needed.\n", + "\n", + " # Hidden layer computation:\n", + " Z1 = np.dot(W1, X) + b1 # This performs the linear combination.\n", + " # TODO: Apply the ReLU function to Z1 to get A1.\n", + " A1 = ...\n", + "\n", + " # Output layer computation:\n", + " Z2 = np.dot(W2, A1) + b2 # Linear combination for the output.\n", + " # TODO: Apply the softmax function to Z2 to get A2.\n", + " A2 = ...\n", + "\n", + " return Z1, A1, A2\n", + "\n", + "# Example: Run a forward pass for the first training sample.\n", + "Z1_example, A1_example, A2_example = forward_pass(train_inputs[0], W1, b1, W2, b2)\n", + "\n", + "# TODO: Print the hidden layer's pre-activation (Z1) and post-activation (A1) and (A2) values.\n", + "..." + ] + }, + { + "attachments": { + "One_Hot_encoding .png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Loss Function\n", + "\n", + "### 4.1 What Is the Loss Function?\n", + "\n", + "The loss function tells you how \"wrong\" your network's prediction is compared to the true label. For classification, we use **cross-entropy loss**.\n", + "\n", + "### 4.2 Explaining Cross-Entropy Loss\n", + "\n", + "Suppose your network produces a probability for each class. For a given sample:\n", + "\n", + "- Let be the output probabilities.\n", + "\n", + " $\\hat{y} = [\\hat{y}_1, \\hat{y}_2, \\dots, \\hat{y}_{10}]$\n", + "\n", + "- The true label is, say, class $c$. In one-hot encoding, the correct answer is represented as a vector with a 1 at position $c$ and 0s elsewhere.\n", + "\n", + "- Here is an example of a representation of a one-hot encoding data :\n", + "\n", + " ![One_Hot_encoding .png]()\n", + "\n", + "The cross-entropy loss is defined as:\n", + "\n", + "$L = -\\log(\\hat{y}_c)$\n", + "\n", + "This means if your network assigns a high probability to the correct class (close to 1), the loss will be small (since log⁡(1)=0\\log(1) = 0). But if the probability is low, the loss is high.\n", + "\n", + "### A Simple Example\n", + "\n", + "If the correct class is 3 and the network outputs:\n", + "\n", + "$\\hat{y} = \\begin{bmatrix} 0.1 \\\\ 0.2 \\\\ 0.05 \\\\ 0.6 \\\\ 0.02 \\\\ 0.01 \\\\ 0.005 \\\\ 0.005 \\\\ 0.005 \\\\ 0.005 \\end{bmatrix}$\n", + "\n", + "Then:\n", + "\n", + "$L = -\\log(0.6) \\approx 0.51$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_entropy_loss(y_true, y_pred):\n", + " \"\"\"\n", + " Compute the cross-entropy loss for one sample.\n", + " Input:\n", + " - y_true: The true label as an integer (0-9).\n", + " - y_pred: The predicted probabilities as a numpy array of shape (10, 1).\n", + " Returns:\n", + " - A scalar representing the loss.\n", + " \"\"\"\n", + " epsilon = 1e-12 # To avoid log(0)\n", + " # TODO: implement cross-entropy loss computation.\n", + " loss = ...\n", + " return loss\n", + "\n", + "# Example test:\n", + "dummy_pred = np.array([[0.1], [0.2], [0.05], [0.6], [0.02], [0.01], [0.005], [0.005], [0.005], [0.005]])\n", + "dummy_label = 3 # The correct class is 3\n", + "\n", + "# TODO: Compute and print the loss for dummy_pred.\n", + "..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Backward Pass (Backpropagation)\n", + "\n", + "### 5.1 What Is Backpropagation?\n", + "\n", + "Backpropagation is the process of figuring out how to adjust the weights and biases to make the network’s predictions closer to the truth. It does this by computing **gradients** (which are like slopes in calculus) that tell us which direction to move our parameters.\n", + "\n", + "### The Chain Rule (In Simple Terms)\n", + "\n", + "If you have a function inside another function, the chain rule tells you how changes in the inner function affect the outer function.\n", + "\n", + "- For example, if , then:\n", + "\n", + " $L = f(g(x))$\n", + "\n", + " $\\frac{dL}{dx} = \\frac{df}{dg} \\times \\frac{dg}{dx}$\n", + "\n", + "In our network, the loss LL depends on the outputs, which in turn depend on the weights. We use the chain rule to “chain” these derivatives together.\n", + "\n", + "### 5.2 Backpropagation in Our Network\n", + "\n", + "### Output Layer Gradients\n", + "\n", + "For the output layer with softmax, it can be shown that:\n", + "\n", + "$\\frac{\\partial L}{\\partial Z^{(2)}} = \\hat{y} - Y$\n", + "\n", + "where:\n", + "\n", + "- $\\hat{y}$ is the predicted probability vector,\n", + "- $Y$ is the one-hot encoded true label.\n", + "\n", + "For the weights in the output layer:\n", + "\n", + "$\\frac{\\partial L}{\\partial W^{(2)}} = (\\hat{y} - Y) \\cdot A^{(1)^T}$\n", + "\n", + "And for the biases:\n", + "\n", + "$\\frac{\\partial L}{\\partial b^{(2)}} = \\hat{y} - Y$\n", + "\n", + "### Hidden Layer Gradients\n", + "\n", + "For the hidden layer, we first compute how the loss changes with respect to the hidden activations. Then, since we used ReLU, we multiply by the derivative of ReLU:\n", + "\n", + "$\\text{ReLU}'(z) =\n", + "\\begin{cases}\n", + "1, & \\text{if } z > 0 \\\\\n", + "0, & \\text{otherwise}\n", + "\\end{cases}$\n", + "\n", + "Thus:\n", + "\n", + "$\\frac{\\partial L}{\\partial Z^{(1)}} = \\left( W^{(2)^T} \\cdot \\frac{\\partial L}{\\partial Z^{(2)}} \\right) \\circ \\mathbf{1}_{\\{Z^{(1)} > 0\\}}$\n", + "\n", + "where $\\circ$ means element-wise multiplication." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def backward_pass(X, Z1, A1, A2, y_true, W1, W2):\n", + " \"\"\"\n", + " Compute the gradients for one training sample.\n", + " Inputs:\n", + " - X: Input vector (4, 1)\n", + " - Z1: Pre-activation of hidden layer (8, 1) => i.e. W1@X + b1\n", + " - A1: Activation (ReLU) of hidden layer (8, 1)\n", + " - A2: Output probabilities (3, 1) [= softmax(W2@A1 + b2)]\n", + " - y_true: The true label as an integer (0, 1, or 2)\n", + " - W1: Weights of hidden layer (8, 4)\n", + " - W2: Weights of output layer (3, 8)\n", + " Returns:\n", + " - dW1, db1, dW2, db2 (gradients, same shapes as W1, b1, W2, b2)\n", + " \"\"\"\n", + " # Ensure X is a column vector\n", + " X = X.reshape(-1, 1) # Shape (4, 1)\n", + "\n", + " # 1) One-hot encode the true label\n", + " Y = np.zeros_like(A2)\n", + " Y[y_true] = 1.0\n", + "\n", + " # 2) Gradient wrt Z2 (output layer pre-activation)\n", + " dZ2 = A2 - Y # Shape (3, 1)\n", + "\n", + " # TODO: 3) Gradients for the output layer\n", + " # Compute dW2 as the outer product of dZ2 and the transpose of A1.\n", + " dW2 = ... # Shape (3, 8)\n", + " db2 = dZ2 # Shape (3, 1)\n", + "\n", + " # 4) Backpropagate to the hidden layer\n", + " # dA1 is the gradient coming back from the output layer:\n", + " dA1 = np.dot(W2.T, dZ2) # Shape (8, 1)\n", + "\n", + " # TODO: Derivative of ReLU\n", + " dZ1 = ... # Shape (8, 1)\n", + "\n", + " # TODO: 5) Gradients for the hidden layer\n", + " # Compute dW1 as the outer product of dZ1 and the transpose of X.\n", + " dW1 = ... # Shape (8, 4)\n", + " db1 = ... # Shape (8, 1)\n", + "\n", + " return dW1, db1, dW2, db2\n", + "\n", + "\n", + "# Example: Run a backward pass for the first training sample.\n", + "dW1_example, db1_example, dW2_example, db2_example = backward_pass(train_inputs[0], Z1_example, A1_example, A2_example, train_results[0], W1, W2)\n", + "print(\"dW1_example:\", dW1_example)\n", + "print(\"db1_example:\", db1_example)\n", + "print(\"dW2_example:\", dW2_example)\n", + "print(\"db2_example:\", db2_example)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Optimization\n", + "\n", + "### 6.1 Gradient Descent\n", + "\n", + "Once we have the gradients (the slopes), we update our parameters by taking a small step in the opposite direction of the gradient. This is the gradient descent update rule:\n", + "\n", + "$\\theta := \\theta - \\eta \\cdot \\frac{\\partial L}{\\partial \\theta}$\n", + "\n", + "- $\\theta$ represents a weight or bias.\n", + "- $\\eta$ is the learning rate (think of it as the step size)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#TODO: define hyperparameters\n", + "epochs = ... # How many times to go through the training data\n", + "learning_rate = ...\n", + "\n", + "size = len(train_inputs)\n", + "\n", + "#TODO: Implement the update_parameters function\n", + "def update_parameters(W1, b1, W2, b2, dW1, db1, dW2, db2, learning_rate):\n", + " \"\"\"\n", + " Update the parameters using the gradients.\n", + " \"\"\"\n", + " ...\n", + "\n", + "\n", + "for epoch in range(epochs):\n", + " epoch_loss = 0\n", + " for i in range(size):\n", + " # Get one training sample and its label\n", + " X = train_inputs[i].reshape(-1, 1) # force reshape (4,) to (4, 1) to avoid error\n", + " y = train_results[i]\n", + "\n", + " # Forward pass: compute activations\n", + " Z1, A1, A2 = forward_pass(X, W1, b1, W2, b2)\n", + "\n", + " # Compute the loss for this sample\n", + " loss = cross_entropy_loss(y, A2)\n", + " epoch_loss += loss\n", + "\n", + " # Backward pass: compute gradients\n", + " dW1, db1, dW2, db2 = backward_pass(X, Z1, A1, A2, y, W1, W2)\n", + "\n", + " # Update parameters using gradient descent\n", + " W1, b1, W2, b2 = update_parameters(W1, b1, W2, b2, dW1, db1, dW2, db2, learning_rate)\n", + "\n", + " # Print the average loss for this epoch.\n", + " print(f\"Epoch {epoch + 1}/{epochs}, Loss: {epoch_loss / size}\")\n", + "\n", + "print(\"Training complete!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "Well done if you are here You finish this day bravo !\n", + "\n", + "if you want to go deeper again, you can try to add more layers into your neural network or try to play with another dataset like the mnist... :)\n", + "to load the mnist dataset you can see [here](https://gist.github.com/alperyeg/ca5e5e9b5ffb442a9ce5caca7c8399c1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day03/README.md b/AI/Day03/README.md new file mode 100644 index 0000000..fde37e8 --- /dev/null +++ b/AI/Day03/README.md @@ -0,0 +1,30 @@ +# ~ PoC AI Pool 2025 ~ + +- ## Day 3: Understand Machine Learning + - ### Module 1: Linear Regression + - **Notebook:** [`linear_regression.ipynb`](<1 - Regression/linear_regression.ipynb>) + - ### Module 2: Logistic Regression + - **Notebook:** [`logistic_regression.ipynb`](<1 - Regression/logistic_regression.ipynb>) + - ### Module 3: Neural Network theory + - **Notebook:** [`neural_networks.ipynb`](<2 - Neural Networks/neural_networks.ipynb>) + +--- + +**Hooray : You've made it to AI !** +On today's menu, we'll enter the wonderful world of machine learning with two major algorithms : Linear and Logistic Regression, followed by a introduction of the theory behind neural networks ! + +> Here's a list of resources that we believe can be useful to follow along (and that we've ourselves used to learn these topics before being able to write the subjects): + +## Ressources + +- [3b1b's neural network series](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) +- [sentdex's NNFS series & book](https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3) +- [andriy burkov's 100-page ML book](https://themlbook.com/) particularly [chapter 4](https://www.dropbox.com/s/xpd5x6p6jte3th5/Chapter4.pdf?dl=0) +- [the math sorcerer's video on partial derivatives](https://www.youtube.com/watch?app=desktop&v=gnkhT3XDU5s) +- [khan academy's video on the chain rule](https://www.youtube.com/watch?v=NO3AqAaAE6o) + + +- [derivating bce loss](https://www.google.com/search?sca_esv=593424282&rlz=1C5CHFA_enFR1086FR1086&sxsrf=AM9HkKnhFXQw46XVx7yP5nyzZOxkebfGWw:1703424283700&q=sigmoid&tbm=isch&source=lnms&sa=X&sqi=2&ved=2ahUKEwiXptP6laiDAxVBU6QEHVYpCxIQ0pQJegQIDhAB&biw=1512&bih=738&dpr=2) + + + diff --git a/AI/Day04/1 - Torch/Introduction_Torch.ipynb b/AI/Day04/1 - Torch/Introduction_Torch.ipynb new file mode 100644 index 0000000..dd99c9c --- /dev/null +++ b/AI/Day04/1 - Torch/Introduction_Torch.ipynb @@ -0,0 +1,880 @@ +{ + "cells": [ + { + "attachments": { + "torch_logo.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![torch_logo.png](attachment:torch_logo.png)\n", + "\n", + "# ~ PoC AI Pool 2025 ~\n", + "- ## Day 3: Neural Network\n", + " - ### Module 3: Neural Network with torch\n", + "-----------\n", + "\n", + "## Introduction\n", + "\n", + "\n", + "[PyTorch](https://pytorch.org/) is the most used framework when it comes to Machine Learning, especialy Deep Learning.\\\n", + "Whether for computer vision or language processing, PyTorch allows you to build the state-of-the-art in AI.\n", + "\n", + "\n", + "Developed by Meta AI, PyTorch is now part of the Linux foundation and is completely [open-source](https://github.com/pytorch/pytorch).\\\n", + "When you hear about deep learning, PyTorch is never far away as it is present in Tesla cars, it is used by OpenAI, Google, AMD, Nvidia, AWS, Microsoft, Meta, Netflix and many others !\n", + "\n", + "As you can see, PyTorch has distinguished itself as the AI framework par excellence.\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why use PyTorch\n", + "\n", + "But do you know what PyTorch is for?\n", + "\n", + "Yesterday, you dive into the theory of machine learning and neural network\n", + "\n", + "Now imagine that you have millions of parameters, a complex architecture and that you have to create a neural network from scratch every time.\n", + "\n", + "Well, **PyTorch allows you to build neural networks very easily**, forget about the mathematics behind it and build complex neural networks in a few functions and parameters. \n", + "\n", + "Excuse me, **don't entirely forget the mathematics**, it's important sometimes ;)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch \n", + "import math\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part.1 The Tensor\n", + "### What is a Tensor ?\n", + "\n", + "As I told you before, the data in PyTorch is in the form of tensors.\n", + "\n", + "Concretely, a **Tensor is an object** that is similar to an array or a matrix.\\\n", + "Actually, tensors are **similar to arrays in Numpy** with a **few differences**.\n", + "\n", + "The main strength of tensors is that **they can run on GPUs** or other hardware acceleration devices.\\\n", + "You may already know this, but AI models are often accelerated using graphics cards.\n", + "\n", + "In addition, **Tensors are optimized to calculate gradients** in the gradient descent algorithm.\\\n", + "If you remember gradient descent is the algorithm that allows to adjust the weights of our neural network and thus to make it learn new things.\n", + "\n", + "In short, Tensors are used to encode the input and output data of our neural networks as well as the weights of our networks.\\\n", + "They have the advantage of being able to run on a GPU and to be optimized for gradient descent." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1 - Build a Tensor from data\n", + "\n", + "In this first exercise you will have to **create a tensor** from the array ``data``\\\n", + "Be careful, the tensor must be built from the array and thus contain the same data.\n", + "> You might want to take a look to the [documentation](https://pytorch.org/docs/stable/tensors.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = [[1, 2],[3, 4]]\n", + "\n", + "#TODO : Tensorise the data with torch\n", + "tensor_data = ...\n", + "\n", + "# Print the info of the tensor\n", + "print(tensor_data)\n", + "print(\"-\"*20)\n", + "print(tensor_data.shape)\n", + "\n", + "assert torch.is_tensor(tensor_data), \"Your object is not a tensor\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2 - Build a Tensor from shape\n", + "\n", + "One of the many interesting features of PyTorch is that you can generate Tensors from shapes.\\\n", + "This can be useful when you want to initialize neural network weights for example.\n", + "\n", + "Create a Tensor **filled with 0** and of shape ``(2, 3)``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : Create a tensor of shape (2, 3) filled with zeros\n", + "shape = ...\n", + "\n", + "tensor_shape_zeros = ...\n", + "\n", + "print(tensor_shape_zeros)\n", + "print(\"-\"*20)\n", + "print(tensor_shape_zeros.shape)\n", + "\n", + "assert tensor_shape_zeros.sum().item() == 0, \"Your tensor is not filled with zeros.\"\n", + "assert list(tensor_shape_zeros.shape) == [2, 3], \"Your tensor does not have a shape (2, 3,)\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3 - Print Tensor's attributes\n", + "\n", + "You now know how to create a tensor.\\\n", + "Create a tensor with random value on the size you want !\n", + "Display **four pieces of information** about this tensor:\n", + "\n", + "* The values itself\n", + "* Its shape\n", + "* The data type of the tensor\n", + "* The device on which the tensor is stored\n", + "\n", + "Don't hesitate to take a look at the [documentation](https://pytorch.org/docs/stable/tensor_attributes.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : initalise a tensor with randome value\n", + "tensor = ...\n", + "\n", + "############################################\n", + "#TODO print the infos of the tensor \n", + "..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4 - Use GPU if available\n", + "\n", + "If you look carefully at which device your tensor is stored on, it will be on your CPU even if you have a GPU.\n", + "And this is normal.\\\n", + "If you don't indicate that you want to store your tensor on your GPU then it will use the CPU by default.\n", + "\n", + "Look at the [documentation](https://pytorch.org/docs/stable/generated/torch.Tensor.to.html) about this.\\\n", + "For checking if cuda is available [check this](https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available).\n", + "And for Mac user check if mps is [available](https://pytorch.org/docs/stable/notes/mps.html). \n", + "\n", + "Add a condition to know if a GPU is available, if it is the case move your tensor on your GPU.\n", + "Do the same for Mac user use MPS." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tensor_device = torch.rand(3, 2)\n", + "\n", + "print(\"before :\", tensor_device.device)\n", + "#TODO: check if cuda or mlx is available and move the tensor to cuda or mlx if it is ; ~4 lines\n", + "...\n", + "\n", + "print(\"after :\",tensor_device.device)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5 - Apply an arithmetic operation to a Tensor\n", + "\n", + "In the same way as for a numpy array, one can easily apply arithmetic operations on a Tensor.\n", + "\n", + "Multiply the data of the Tensor by **42**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tensor = torch.ones((3, 3), dtype=torch.float)\n", + "print(tensor)\n", + "\n", + "#TODO: multiply the tensor by 42\n", + "tensor = ...\n", + "print(tensor)\n", + "assert int(tensor.sum().item()) == 378, 'The tensor is not multiply by 42.'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6 - Reshape a Tensor\n", + "\n", + "Again in the same way as a numpy array, you can reshape your Tensor using the ``reshape`` method.\n", + "\n", + "Turn your shape tensor ``(3, 9)`` into a shape tensor ``(3, 3, 3)``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tensor = ...\n", + "print(tensor)\n", + "\n", + "### TODO: code here (~ 1 line)\n", + "tensor = ...\n", + "print(\"-\"*50)\n", + "print(tensor)\n", + "assert list(tensor.shape) == [3, 3, 3], \"Your tensor does not have a shape (3, 3, 3)\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Part 2: Neural Network Layers\n", + "### What is a Neural Network Layer?\n", + "\n", + "In PyTorch, **neural network layers** are the building blocks of deep learning models. \n", + "\n", + "There are two fundamental types of layers that are widely used in AI:\n", + "\n", + "1. **Linear Layers (`torch.nn.Linear`)** \n", + " A **Linear Layer** performs a simple mathematical operation:\n", + " $\n", + " y = x * W^T + b\n", + " $\n", + "\n", + " Linear layers are used to map input features to output features and are common in fully connected layers of neural networks.\n", + "\n", + "2. **Convolutional Layers (`torch.nn.Conv2d`)** \n", + " A **Convolutional Layer** is primarily used for images. Instead of processing individual features, it processes small patches of the input using filters or kernels:\n", + " - **Kernels**: Small learnable tensors that slide over the input, detecting patterns like edges or textures.\n", + " - **Key Parameters**:\n", + " - **Kernel Size**: The size of the sliding window (e.g., 3x3).\n", + " - **Stride**: The step size of the kernel.\n", + " - **Padding**: Adds extra borders around the input to control output size.\n", + "\n", + " These layers are essential in tasks like image classification, object detection, and image segmentation.\n", + "\n", + "### Layers in PyTorch\n", + "\n", + "In PyTorch, layers are defined in the `torch.nn` module where you can find the doc [here](https://pytorch.org/docs/stable/nn.html)\n", + "\n", + "In the next sections, we will explore these layers in depth with practical examples, good luck !" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 2.1 Linear Layers \n", + "\n", + "\n", + "\n", + "ou might want to take a look to the [documentation](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear) of `linear layers`! \n", + "### Step 1 - Build a simple layer\n", + "\n", + "\n", + "In this first exercise you will have to **create a layer** with that take 3 features and output 2 features !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create a linear layer with 3 inputs and 2 outputs\n", + "layer = ...\n", + "\n", + "print(layer)\n", + "print(\"-\"*30)\n", + "print(layer.weight)\n", + "print(\"-\"*30)\n", + "print(layer.bias)\n", + "\n", + "assert list(layer.weight.shape) == [2, 3], \"The weight of the layer is not the right shape\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2 - Apply a Linear Layer to a Tensor\n", + "\n", + "- Your goal here is to create a tensor with random value of shape (1, 4)\n", + "- Pass this tensor to a linear layer that output 2 values !\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Create a tensor with shape (1, 4) and pass it through the layer\n", + "tensor = ...\n", + "\n", + "layer = ...\n", + "\n", + "output = ...\n", + "\n", + "print(\"input tensor :\",tensor)\n", + "print(\"-\"*30)\n", + "print(\"output tensor :\",output)\n", + "\n", + "assert list(output.shape) == [1, 2], \"The output of the layer is not the right shape\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Congratulations you make your first forward operation of a neural network ! (remember the forward function you made yesterday is the same as here :)*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3 - Chain two Linear Layers\n", + "\n", + "In the same way of above, create two layers and forward a tensor to it !\n", + "\n", + "- The input of the **first** layer size need to be 3 and the output of the **second** layer need to be 3\n", + "- Try to find out what are the value you need to put beetween the both." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Create a neural network with 2 linear layers\n", + "\n", + "tensor = ...\n", + "layer_1 = ...\n", + "layer_2 = ...\n", + "\n", + "assert layer_1.out_features == layer_2.in_features, \"The dimensions between the two layers are not compatible and need to be the same\"\n", + "\n", + "#TODO: Create a forward function that takes a tensor as input and passes it through the two layers ~3 lines\n", + "def forward(x):\n", + " ...\n", + "\n", + "output = forward(tensor)\n", + "\n", + "print(\"output tensor :\",output)\n", + "assert list(output.shape) == [1, 3], \"The output of the forward function is not the right shape\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4 - Understand Batch\n", + "\n", + "- create two tensor, one is the shape (1, 3) and the other is shape (3, 3)\n", + "- pass it to your model and compare the result, what can you find ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Create to tensor with different shapes and pass them through the forward function\n", + "tensor_shape_1 = ...\n", + "tensor_shape_3 = ...\n", + "\n", + "result_1 = forward(tensor_shape_1)\n", + "result_3 = forward(tensor_shape_3)\n", + "\n", + "print(\"Tensor with a batch of 1 :\",result_1)\n", + "print(\"-\"*30)\n", + "print(\"Tensor with a batch of 3 :\",result_3)\n", + "\n", + "assert list(result_1.shape) == [1, 3], \"The output of the forward function is not the right shape\"\n", + "assert list(result_3.shape) == [3, 3], \"The output of the forward function is not the right shape\"" + ] + }, + { + "attachments": { + "understand_batch.png": { + "image/png": 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E50yUxKJSiU0Z6xnTVfuhrH9itrg7uuxjMqZeKGP2vdhuD9eG77VzZ/5NkkumbfXlVt94gBEiS9xuZ8k7/qGtPi8niE6B1s8ekfrn/+p3c3HpaZK2/5kSN6bE8zsZE5fkGdOy6gFpePkGY9nKgrMWSXzWBL9zRGPHcP3dRqMV94QAAggggAACCCCAAAIIIIAAAggggAACCCAQ2QIEriL7+TF7BMJWgMBV2D6aiJxYR0eHbNiwQfr6+iQ2NtbzXVRUJBtVxZGEhARJTU2VpqYmGTNmjLjdbnG5XBF5n9ty0h2rX5Omd2+VzjVfGeET3znknXqLJBaogAqfqBDY+Nr/SMsHy/zuJf+0uyRh7N5+/Z6Ovl5p/eJJ6fj+VUnZ+YeSvP1053FD3Fv/0v+T1k9ess/qSoiT4ksq7PZgN3wDV/HZOVJw5nODPR3HRblA9QOqulV9vXGX+l0suvDlgFXX3J1N0vLpQ+q4ryRt3wtVdauJxvHR3Biuv9toNuPeEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCJTgMBVZD43Zo1A2AsQuAr7RxT2E+zp6ZHe3l6566675KuvvpLW1lZP6Kq5uVlyc3M9oavu7m7ZfvvtPeP0d05OjieAlZmZKcnJybLffvtJZ2enZGVlEcRST7y3rVaaP7hThViWGhWLgr0MY8pOl4wD5gYbwr4IEqi6/SC/Z5+y2xTJmf7PsLsL3+CGnmDBmQ9KfPYuWzXXNTdPlr5et32O2NRkKbrgNbvNBgKWQKCKcDkzrpWUnY6zhvHtJTBcf7del2ATAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGwECBwFRaPgUkgEH0CBK6i75lu6zuqVxVF/vrXv8rixYsDXjouLk50MEt/dKWr+Ph4T9BKH7vDDjtIfn6+7LzzzlJQUOCpjHXEEUdITU2N7LLLLp4qWDq4Fe0fd3e7tKy6T1o/flR6VEWwgX7GTD5VMiZfbR+mlyDsWve+xCSkSmL+/nY/G+Ev0NtaI2vv+pHfRAvPfcSzLJrfjhHucApu5Bx7jaqyNXOrZlZ1a5m4uzf9bugTuRLjpfjit7bqnFFxsLtHOmvfl76u1k1LibriouK2tuYmWj5ZKBtfutk4xVBVWjNOGkWN4fq7jSIibgUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgSgQIXEXJg+Q2EAg3gVmzZklFhbn0U2VlZbhNk/mEsYBeInDhwoVy0003eYJT33//vSc0pateDeSTmJjoqXKllx3Ux+oqWMXFxZ6qV8cdd5zsvffenr6MjIyBnDbsx3bXfyGNFX+V9m8+CrpkoO+NxMS67Oo/rvhYyZv1gMRnTfAMq19+lbSuetU+JLFoe8k7+WHVjrH72AhfgeYP75CGV283JhiXmSGFZ79o9IVLwym4kXX45ZK25zlbNcWq2w9WVb467XPExLlk3OyVdns0brR+8bjU//vP9m+FKylBVRN7QmJTxo5GDvuea584XTq//9Ju643UvY6Q7Gl/MfpobBEYrr/bLVdgCwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB8BAgcBUez4FZIBB1AlbgqqyszA5eEbiKusc87Dek35n169dLS0uLZ1nBzz//3LOtlxd8++23JSUlxbPMoMvl8iwZOJAJ6YpYXV1dMmnSJE8ga8aMGZKWlibl5eWe5QsjdRlCHbSqf/GX0lWzOmQOV0K8pE8+2xNkccXGS8unD6kKQG2SNvFUcSVne87T9uWTsmHZH/3Ombr3dMk+TAU1+IS9wP9n7z6goyrTP44/pPdACKkUKaKIKCBSFQSRFVFRURERGxZEVNC/u+u6rruurl2xYi8ogthFUFQWFRUEERsuIFKEkBAC6T0k//e5OMPcmUmDJEwm3zkH55b33vvez51JPCe/8zyZ715kwiPrbfOMHXKJxBw3zbbNV1a8BTdiB0+SmH7XH9QUd7xwkuwtKHSeo1VAK2k/baVzvaUtaKvRHc+PcYatHPcfZFqxJk/62LHaIt/TnjLV0Mr2V0NThKRJ80wItWuL9KjLTTfW97Yu12YMAggggAACCCCAAAIIIIAAAggggAACCCCAAAJNKUDgqim1uRYCLUiAwFULethNeKu7d++2qlFt3rxZtG3gunXrZMuWLbJ27VrZsWOH1U7wl19+sdoLapiqVatWUlVVVecZ6vhevXpZIaxOnTpJz549rXaEGuzSyli+/CrN+NZUn3rN/FtW52kGRkVKzIArJKrnRHNMzVWqsj//uxT8sNjruePH3iPhnU72uo+NviOQ/sooj7aSSZPmOiuY+c5M983EW3AjqvdoaTP0joOaavorpxiHnP3nMB/9Dtet2r/ewpZKtn8pu96e4fWuo479k7QZ5hm09DrYDzdue+x4WxCNami1P+TG+t7WfmVGIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQtAIErprWm6sh0GIECFy1mEd9SG+0vLxcgoKCRCthBQYGys6dO63qVF9++aXoPg1l/fzzz1JSUiJpaWnOIFZtk9bqV9p+sHPnzlZga8iQIRITEyPXXHONaKvDlJQUa7sGtA71q8CErHK/eNRUpKp7q0VtBRgz+CYJSxlY5+lnfXydFK9b4X28YUg4b5aEJvXzvp+tPiHg0UrPtI9sf63vttLzFtwI79pb4sc8e1CeGXPPkPJdGbZzdLi+5QauijYukN2Lqg+xxZ54mcT0mWrzagkrlWX5kvbUCNuthiS1l8Tz37FtY8Uu0FjfW/tVWEMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA4NALELg69M+AGSDglwIErvzysTabmyouLraqXWVkZFhVrr799lurNeHXX39ttSTU6lcbN260glg6NiwszFqu6QYjIyOtKlfagvC4446T/v37S2hoqLRr185qbVjTsY21L2/NLMld9kKdTq9t0yJ6nCixA2+WwMikOh3jOqgiZ7Okv3K+rdqL634NcSWc+4brJpZ9TCDtqcGmPVq5c1YhCSmSeMF7znVfW/AW3GhlQmKRR58ie0v2SMXuLbI3L8d2T1qorZVpMarjwrsPlbgR93rcVubb46V0+ybb9pYcuJLKCtnx4gjZW1hsM3GsBAQHSuo11YQtHYP88N1qtficabXo8ooZcL7EDrjZZQuL7gKN9b11vw7rCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggcagECV4f6CXB9BPxUgMCVnz7YZnxb2lowOztbNGyVk5Mj0dHRsnDhQqmsrLSqX2mVrO3bt1vLWi2rsLDQ693qvqioKElOTrYqZg0ePFji4uLk9NNPt8JXWh2rqV5ZCy6T4s0/13i5oDZtJLrvxRLVY7xIQHCNY2vbWbLtC9n94S1SWVLmMTSodawkX/ypx3Y2+I5A2jNDbM8uvHs/iT91lu9M8I+ZVJYXSkXeVsn+9G9StjPtoOaXOOEFCWnXy3aOrIWTpfi3H23bWnTgykhU5G2TrIVTPSp/OZBaok9laa6kPT3SQWC9x/3pFok84hzbNlb2CTT29xZnBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQR8TYDAla89EeaDgJ8IOAJXrrejgRZeCPiiQG5urtWS8Mcff5SioiJZtmyZVQlr165dsnv3btm8ebO1XUNbrq8AU0lHA1sauNLladOmSevWrUVDWBUVFZKamuo6vMGXd84/R8oytnk9r1b4SbjgZQlpe6TX/Qe+sUryf3xB8pY/L5Wl+6olafWsmCGXm7ZjUw78tBzZ6ALuLQUjew6TuJMfaPTr1nqBqr2StWiKlG772bTGrKi2ilqt5/EyIHHC8yZwdYxtT9biaVK83t5KsSUGimwof6yU/P6ZZH9xt1SYdqzWy1QMCz+sp8Sf8dIfI1rOmwaI0madZLvh+DPvNh72EJZtQEtaaeLvbUui5V4RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEmocAgavm8ZyYJQLNToDAVbN7ZEz4DwENVWmIylENKyUlRd59912r5eCHH35o7fviiy+kjakcpWPcX1r5SqtndezYUQYNGiTjxo2zwlixsbHuQw96PfOdC01I5ddqzxPe+WiJP/0502MtsNoxB7OjsjTPOjwgNOZgTmM/dm+ZlOdukfKcTVKR/atU5Gw1rRDPlbCUgfZxrNVbYMfzQ21t4yJ7nSxxw++p93ka+oCMuaebyko7G/q0En7kQIkf+bCp7BZkO/eeJTdL4drPbNsIXNk4pKqixLRqzJfAiHb2HS1prbJctj0+2HbH8WPvk/BOw23bWupKU39vm8pZKznmrpgpgWGxEjfyfgkIj6/20pXlxRIQaCpHuv2MqfYAdiCAAAIIIIAAAggggAACCCCAAAIIIIAAAgj4lQCBK796nNwMAr4jQODKd54FM2k4Aa1+pa0G161bJ1lZWbJixQqJiYmRVatWSXl5uWzYsMGqlFVcXGy97927V4YNGyYJCQnSt29fqw3hcccdZx3TELMq3rpEst77a42nCggJkpiBl0l07yvNOFOuxgde2qqrZPtXpl3cGqnI/d2EqnaYIFCOVJWWSdXeSq8zbDPyRok6aoLXfbVt1PBIzvK7peS3ZVJpnk1VVaUEhIVLcHwniTjyLInsfnZtp2jy/Trnivzt0io4UoKiksz16//s9By53z4iUlkhIYl9JHvJXbaWgqEduknC2XOb/N5cL1hRkC7pL5zpuqney1rNLSiunYSm9pHQ9oMlJL6nBMV0MOfxbpb9+d+l4IfFtuscisBV6Y5vJOere02wMNMEnMqllamSFxgbLyHJR0ts/xtbdtjJ9nSabqUib7vkffekBEa2k5CE3ubn659tF48deoXE9L7atq2hV5rD5+JQfG8Pxll/FmZ/fqv5+Zdjfh/eZCo/HuX1dLmrHjbVG19z7rNCm6Mec67rQu6qmVL083tSUVDgrMYXEB4qwe06StQxkySiy2jbeG8rFfk7JGfZ7VKWvkGqykqtIQFRMeYcXSWm7xTz2TvW22FsQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEPAxAQJXPvZAmA4C/iJA4MpfniT3UZ2AVsHSNoLajlDDVjt37pT169eLts5cuXKlrF692morqKGrsLAwq0JW586drbCVth5s166d9O7dW/Lz8w8qgJX/w7OS8/kz1U3TuT0gJFii+o6T6D5TJSA43Lm9MRfy1jxpWrctllZhkaYASKgJEGXJ3pysfW3j6nnhgLBQSb3qy/odZVpe5a1+QvJWzjGBFu9BLj1hUEy0tB3ziGk916t+52/I0aaaTsEvcyT/u1dlb16eVFW6ta8MDpSAiEgJP+JPEt3rYhMI0RBWza/0V0aZMI9nFTbHUUHRUZJ82VLH6iF5L8tcIzvnXVWva2u4ITS1h4R1PNG8D5HgNl3rdXy2CToUrFlkO6YpA1flezbInk/+bAKHabY52FZMVizq2NHSZugdts3NcUVDTAU/vSRF6z6RvSa0GpLUwQT9XpNWQWG+dTsmmLjtiUHOEI23yUX2PNG04XzI266D3tacPheH4nurwBqcKsv8QQKjUs3P7fZ1Ns98Z4KpBrlx33jz3Uq88BWv7XazFk6W4t9+dJ5XA8upU5Zb6xWm6uKu9640IeF9lR2dg1wXzLlTJi+qNiypYeM9JvBZvH5FjZ8zDcPGn/a0NGj1SNd5sowAAggggAACCCCAAAIIIIAAAggggAACCCDQIAIErhqEkZMggIC7gCNwpS3Vli/f98cqDaLwQsDfBTRAFRkZKe+//75s2bJFXn31Vdm1a5d12+Hh4aLVrzSo1bNnT0lMTLTee/XqJaeccsoB0xSuf1uyP7232upQriduFdBKwg7vJ60H3CxBrTu77mrQ5criPZL23J9q/KNyfS4YGBlu/pD9RZ0P2VuUKRmvnmMqmuyrHlLbga2CAiT5kvfqFGSq7Vz13V+0cYFkf2KqT5XvrfOhQaalZdwpd0lo0vHVHrP98f4ewS33wcHtkiSs84kSfthwCTUVfUwaz31Io65rACHt6ZF1vkZQXJwkX2SvTlXng/8YmP2FCVx97x64Wmn2mrREI79yv3lA8r55vc5XiTx2lMQNu6vO45tyYEXeVhP2vN1U+tllwordpfXQf0tASJRtCu4Vgxw79z3Hj8xq45s7rlnbuwZ5ds67ouZhZrqhHY80bQVPNIG/kyQ4rnvN4+u4tyk/F2W710ne1/dK+Z7fpdIE4CrLK6zHEBBkWs8GB0tgeJSptHS4tDnxH9W20zsU39vK8kLJmD3a2RY1wIRQky9fbEJJtbTrNcHbbY8NtD2J8O79JP7UWbZtuuIeuNLfC+2nfmOqMG6SjDkT6vQ7tu2Yf0pE1zEe59ZWhVnv/V+tP5MdB1rfkYnm51QjtQR2XId3BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQMXIHB14HYciQACNQgQuKoBh11+IVBaWiqhoaGiFaw0TKgVr3Rd2wpmm6pCGrL6+uuv5ccff5ScnByrnWB8fLz89NNPtvuPiIiQU089VS6//HLR4NWBvipL86wWRYX/M1Wg7MWRqj1lcLsEE5K4VcJSB1c75kB35K15SnKXPX+gh3scF93/XGk98C8e271tqCzOkvRXxtra53kb574tqE1rSZ70ifvmRlvXeWYtvEZKd2w54GuEdz1G4kY+7LUSyrZHqw9jVXfBgNBgCWqTJMGJPUyY6zgJTRkoQdEp1Q1vkO2F696QPR/ft/9cJtQS3KatmL6cUr4rc/92sxSa0kkSzn3Ttq2+Kzlf/VvyV79vOyx1ylKPsJBjgFbzKVz3rpTv3mBaUcZIRPczvQYqHOOre68ufFTdeMf2+LH3mYDPcMeqz7zbqgaZWYW27yIJ5+wPk+X/+LzkfPZUtfONPn6ctB7012r3N/WOkt8/k13v3ly/y5rPamBEuGlp2d5U7jpGQpP7mX/9vX4fqztxU30uNASb9dG1+ys9VTehP7YHRkaYkOvn1Y5q6u9t7qqHTLu/ubb5RPc7S1oPvtW2zX2lbNePsnPuZNvm6qr7eQSuTEA55eqlpu3pKVJZWm47R3UriROeNwHEY2y7S9K+ll1v31Dn382OgyOPOUXiTvqPY5V3BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8TIHDlYw+E6SDgLwIErvzlSbas+9DWgPpPQ1BZWVmioaqQkBCrKpW2DNR92kLQ8U+DVHv27JHvv//e2ldSUmK1ESwrK7NCV656HTp0sEJYOsb9NWDAALnxxhtl4EB7FQ73cXVZryzLF62WUvjTRzW20XM9l7b4ajvqAVPxqovr5mqXK/J+l5Lfl0pgdHsTBDnZ67j8n16UnKVPet3nutFqD5d8uOwt2GNaRe1w3eVctqpbXW5a39Wh0oeGCtJfOdN7ZSsN8sQnmaotsSZ0sN7rH79rCt44J9QAC4Xr3pTsJffXqWJKbZfTSi8J42d7VNtJe+YE7w61ndBtf0BwkIQfPlBi+s8wbbw6uu1tmNXSnauleNMnJqxynIR3NOGigCBTySZDdjx/hu0CoZ2OlISxr9i21XfFW8AlZfICr9XN8lY/Jrlfzfa4RGTPoaa13IMe26vbkLf6cXOel73u1nafoR16SFnGr87qPa4Dq6vG4zqmoZY1uFm8ebGpwlMhUT3Or/E7t+OFk8z3ttB5aW3NmXzpf631OrWcM9/H1Kv/a4Ju0c5zHMqFivwdkv7i2AaZgv7Miuh5usQcd12NLVyb6nOh7Qoz518ilWWmmlU9XrV9zpvye7tz/jnmO7LNNnsNnMaPqTnYW/TbQtm98J+24wLCQkyL2q9s23Ql68MpUvzr6v3bzWc0/IiBUrzOtAB0e+nPxdAOR5pfKuGyNzvN/L4tleh+l0vkkeZ74/IqTV8pmW9e6/X3TavAAAlJ6WJ+TheYcGmGy1H7Futb2dHjBGxAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaFQBAleNysvJEWi5AgSuWu6zPxR3rtWltE2fvmugqVWrVta/sLAwazqFhYVWcCrYtEvSNn9Fpo1SVFSUbN68WdLT062QlFal2rRpk7WsQauMjAxJSkqyAlUFBQXWeXbv3m2FrTSQpdfR9oD60utVVXmWlQo0FXq0AlZ1r5NOOkmOOuooufjiiyUmJsZqRVjd2HpvryyXvO+fkfyVc8wf2etWmSN28CSJ6Xd9jZfSdlQ7X5vk/ONxm5E3StRREzyOqSwr2FcVxO0P/AERYaZK0ZESdtgIUyXoNKsdVPGWTyRrwd+c53Q9mf5RO2H8yx5hItcx+5erZMcLw20hEMc+bc+UcPbLzlCNhnl2vX+lxx+54079m0R2P9txWKO856y4zzyXN2o+t/lDvwbhgtseLhV526Q801RRKymr9hhtfaVOIW1NAOCPl1Z22fXONTUe5xhbp3czp8hepuLKif80ZX1C6nTIwQ5yr9IVktReEs9/56BOm//Ds6YV3jO2cyRf/Lo9cLi3TDI/mCylW9fZxrmutBlxrUQdfanrJq/L2vJzz+K7PfcZz9bDpkj0Mfur7xT8MtcE8R6yfReaMnSR9tRg58+L8G69Jf60Zz3n/ceWnfPG2kKSASFBkjpluakElGeCS3+qU7in9fCpEt3rsmqv0dQ7Cta+ZoKiM+vc9q22+en3svWw6ySq50UeQ5vqc1Gy/UvJevfGA76n8MP7Svzopz3mX9OGxvjepj1tPptuVaZCOx4uCWe9VtNUJO+7JyT3y5dsYzTkmDrla9s2XcleZtqNrjFt/Gp5xQ69QmJ6X13LKLF+dmfMHufVPvyIARJ/ykwrXKonKs1YZVoO3uBxj6nXfGZCe5G1XosBCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAk0vQOCq6c25IgItQoDAVYt4zI16kxqecgSntGKUVpcKCgqyqk5pYEr3a4UpHaMVp9LS0qx9GpbSIJVuO/zwwyUvL89a17BVfn6+ZGZmWkEpDULpWH3X7XoeDW05KlBVF6Kq7qYdga/OnTtb4Smdn85XWwq6h650LiNGjJDhw4fL+PHjretWd96G2K5/2M9f/ZyUZ+2q9XQa8kk45zVpFbQvrOZ+QNGv78nuD+90bq4pDKLVpgrWzbP+WBzUuquEmVZb7kGdmlotadunxAveMBWp4p3Xq2nBW8spHR/e1QRHxngGRwo3vCN7PvqP7ZTVBchsgw5yJf2VU6QiO6fas2jQJW7kQx6Vfyrytpq2kXeYSlA/2gI5jhNptZSkS96VoKhkxyYzbq/o8y/8ef5BtS7cf0LjeQABDNfj67O8/fH+trCCBvZSr1hWn1N4jC383+uy55MHbNsTJ7xg2oD1srZV5G2XzDcu9FptynaQCUylXO69MpZjXFVFiex49iSpLLcHL/VZJZz/nPOajvH6vuP5obZra5vH1Ks9wyGuxzTIsvmsbHvMXmUv9epPrVCkt/NnLbxCin/7wbmrlWm/1n7aSsmYO8ajFaRzkNuC/rxJPP9tt62HdlUDYwU/z5bCH9+Sivx9YduDnVHcqD+bykfnOU/TVJ+Lkm1fmDaJN3n9eaGTCU3tIqEdB0lw684SaH5uBLfuJlmLp0np778656oLyZe+Y6rbtbdtq2mlMb63258Y4FERsO3pd0hEl9E1TUXSnjWV/opLbWMCwkJNhSvTftft5S2M6TZE2p7+b3PNU903e133VpVLB7YZMc2ENS/xOCbro2ukeMO3tu2pVy+pV4tK28GsIIAAAggggAACCCCAAAIIIIAAAggggAACCDSqAIGrRuXl5Ai0XAECVy332R/InWt1KA04aWhKq0Y5Kkdp9aldu/aFhLTF3++//25Vp9IAllaj0qBUZGSkaOUpXdYgk4abNOzkeIWGhlrVp/QYfdU3SOVapSo2NtY6h1a86tixo2i4SueulbB0PTEx0QpbafvAp556SlavXm3N2TrI/EcrY40dO1Z69eolEydOtAJfOr+memmQJG/VI+YPuss8AiCuc9BqUMkTFniEo3SMe+DKEbJwPb6uy8WbP5asD271GgYITelkgl9zTfWP4DqdTsNdO54/1RbO0QNDOx5hKqC86nkOU8Eo/bUxHsGnpEnzJLhNV8/xDbgl/cXh3oMcJsDT5mTvFcNcL7+3aJepxPIvKV7/jetma7mmAEv253+Xgh8W247R5xdoqr0FRMSaUEKe+WfCjG5VyWwHmJXofmdJ68HmuTXByyO4UU0rsPpMpei3RabF2O22QxyBq8riLEl/2bSkrGNVuNra/e1Z8n9SuPZz27XEPOfE8SZslXCsfbtZ8za3kGQTSjqvCUJJXgJXiRd4n6dOPPPN82whPv0sRfQcYdqZLvG4r8CoSPM5a2Nawm237zMWHa5d7qzyY995qNeqJP3VU6XCtI11fWkLz4DoWNP1Mkz2FuWaCnLFtbZvTRj/lIQmHuc8TVN8LrSKX/rLY73OTUOTrU+4XYKiU5xzciy4t4rU7VG9T5U2Q//tGFLre0N/b8uzf5OMVy6wXVdDi+2vXWG2mQ9RNS9t3brn43s99ga3jZekiR96bC/a9KHs/uAfHtsdG6KO/ZO0GbY/cOzY7u3dqtz4/t88drUedpVEH3ulx3Zt1Zvx6nm256WftdRr9B55IYAAAggggAACCCCAAAIIIIAAAggggAACCPiiAIErX3wqzAkBPxAgcOUHD7GBb0ErTmm4SENRGzdutEJVurx161arfZ8GpdauXSuffvqpREdHOwNYOo2QkBBxBKbcp6VVpPRYby393Me6rjuCVxqo0vPru85HQ1P6ru0IW7duLRqy0n/a8k/fHdt0f7du3azAl1bN0mDYunXr5Ouvv5b//e9/smXLFutyOk7ve9KkSVb7wDFjxljz1esduleVFPwyb1/rrL37w2mu8wntdKQkjH3FdZO1XLRxgexedIdte4dp9Q9MeKsu5ThpeNdjTUWq5xyrdXrf/cn1UvQ/Mw+3lwZiovtcYQs76B/hc5bN9Kh6cjDhMbfL1riatXCyqQxkqlS5veLP/I+EH3aK29bqV3O+utNULnvPY0DqlYtNVbA4j+3eAgDR/c424Sl7KEDbQRb9+q6UbPncVEXbZFo0Fpggm2nbado7Bid1Ne3Fnqi26pHHRQ9yw7bHjrcF8hqi2pNV9ecdU/XH5ZU48WVTwaeTZMwebasu5TLE+6IGhqZ+5TWcWFmSbarrjLLNX0+ibeZiBl8u0abCjaOSnIZjrBCdW3UbHR99/DhpPeivutjIryrZ9qipQufyajvmX1brT5dNzsX0V0aZwGK2c726BatS3cQFVkAp7ZmhHmG2xImv2FphVneeQ7F9z5KbTGDuC9ulEy94xoTl+ti2VeTvkMJ1popc2kqp2L3dBLGKrd9hrUzruogjR5qw0j/N+H3BoCb5XJjwXPps83xy82zz1JXYEy+XmD7XeGzXDVrdK+3pkz321VTJ0GOw2dDQ39uyzDWyc95VtktZodxz37Rtc19Je+5EqSwqcd9stWv1VlmtbNdPsnPu5R7jdYNlcPlS8xjr9rvbW3BNzxN93FiJOnby/kqEJvybs2qmFKx+y6OCV3C7JEnS8DMvBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZ8UIHDlk4+FSSHQ/AUIXDX/Z1iXO9BKUlqNSqtMaeu+PaYSiAaZtFWfBpC09Z9j+5dffmm12HOs63EaotJzVFRUWBWqNISkrQMdISgdE2eqLWllKB2n59Z/GrLSY3W7hrO0wpUepxWxanrpeTX8pPPq37+/JCcnW+fS+fft29cKW+m54+PjrevquPDwcGnTpo01Rw11aRhLXzofvZ6Gx15//XVr/to+UNsc6kuP0zaDo0aNknHjxsmJJ55obfel/1QUmApib0+Sipxcz2mZbED7a5Y5AyGOAVYLwLducKxa794CCLYBbiva4m7P4rvdtu5bjTx6hMSN8KxI4nXwHxu1PVfaU0M9qlu5HqPVUFqZwJCYgFlleYXrLudy7JBLJOa4ac71xlrIWjzVVKdaZTt9aIfDJeHs12zb6rLiLfRSXQWVkh0rZNeb19lOGzNogsQef6Ntmy+teFTKMQGW1CkH116vNGOVZM6farvNxAtfkt0f3ehRzUgHaRAvesAFUrp1ua2ik+MEbUf/XSIOH+tYdb5nf3GbFHz/kXPd20KAuR/zg8IEXUzLsyrPERpyS7lyqcf30HNkw2xxD8p4C+Q5ruStVZtjn+PdGbYKibI2eQsw1Tdo6Dh3U7xnf3G7eYaLbJc62IBYU3wu8n98XnI+e8o2b11pPXyqRPe6zGO7Y0N1x+n+5EvfNqHEDo6hNb439PfW28+uiCMHSdtRj1Y7j4Jf5kr2pw953R+acpgknPuGxz4rDPeMCUl6ebU7+0EJ6zDUyx7PTSVpX8mut6Z77nDZot9t8z87UmX+X6aq0suX34x1VN5zOYxFBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAR8SIHDlQw+DqSDgTwIErvzpae67F0cFKQ08/fLLL85Wf8uXL7dCRlrBSgNSGrTS9n4aXNJwla5roElDWLpNz6NhKQ01aWCqpKTECjfpugaUtNpUQkKCFXDS9SjT7kyrYGm7QT2HBpr0GG3rp+EpRxDLEdxq27atVZ1Kw1J6Tv2n7f40uNXZtADUilM6pk+fPta8dM66Te+rLi+9rla00mpWjqDV999/b1Wtchyv8zr55JPlrLPOskJcvXv3duzyvXdTCSXrgyukePPPHnNrM9KzvV1F3jZJf+kc29iaKqbYBpqV6lo86bjo/udJ64F/dj+k1vW8756Q3C9fqnVcTQOC2rSW5Emf1DSkwfZlLbjUeK+1na+6kJRtkJeVvDWzJHfZC7Y94UccL/F/etK2TVe8VW+JGXC+xA642WOsr2zY/uSAerfY0vss27lawjoOt6pWud9LafpKyXzjWttmDQZV5BfYtumKth5rd/ZsCYxoZ+1LmzXII7AX3vUYU5HteY9j02YNrLF1p8cBXjbEn3m3qXo20suextnkfn+h7buY1p6ve72Y+7NxH6TVyJIv+1gC/ghb6X5v9rWFgNzP25TrOSvuk/yV9mCOhvNC4nse8DSa4nPhrbJTrZUDK8sl7ZlhHhXIHDdan1ai7p+NurTGq+l7660qXU3t/TQ4ZbWYraaCY02tV7c9aqrqub3050PyZUvdtla/mvnW+aba2ebqB9RhT+QxIyXuJO/B5DoczhAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBJhAgcNUEyFwCgZYoQOCq+T51DSBp6Gn79u2iISoNSGnlqlWrVjkrV33++edW4EmrS2kASsdoOEpDSxqu0nCTvjTspK34dJujNZ++a+BKq1o5WgHqugaZNPikQakNGzZItmlVpWO2bdtmw9Qwk1ac0vfDDjNVKkw4y/XcjtZ/jjEa8urUqZM1P72e3pvOtb6vjIwMq5KWhs00aKXz1daBri+9plbK0s+/zq179+6uuxtuubJCCv4337R62yHB8T3+aPlVt8BYdZNIf3G4R+Akqs9p0ubEf9kPMdfe9vgg27bQDt1Mdaa5tm3eVrTl1p6P7/e2S1qfNEWij5nsdV9tGzPfHi+l2zfZhmlFq6pq/thuG2hWtG1TwrnzTMu8SPddjbLu7Y/xbU/7h0R0O6Pe18v68Gop/vU723HVVeqqyNtqwnLn2sbWFFqwDTxEK2lPDbYFQGpr++gakNHWfe2vXubR7s9btRxvt2e1LDtnnqlAZSrR/PHyVqHJW7u1irzfjfU4x2H73vUr6r2QjX2crpmxbU/1XjnLc3DDbXFvg6ZVeFKvWe71At6CKc6BZv6JF7wgIe16OTc5FtyrH0Ufd6a0HnKbY7dPveetedIEGl/TiRDIAABAAElEQVS0zandOQ9LWPsTbNvqutIknwvTom7bE0NsUwqKiTaBUlNtLTDEtt11pbbKWwERYZJ6hfk+1eHV0N9bb4GriB6Dpe0pj3idTeZb5ndCmv13gutAy+PS/7puci5vf8KEPN1+d8QOudhUP7RXB3Qe4GXBPbhoDanH9z/y2FESN+wuL2dmEwIIIIAAAggggAACCCCAAAIIIIAAAggggIAvCRC48qWnwVwQ8CMBAle+/zAdFae0zV5WVpb1TwNPCxYssNrkacBIw087duywWvwVFhZaFaw0XKXVpDS0pGEnDUvpttNPP92q5qTbBw0aZIWg9Lzagu+II46wzqkBJT3n6tWrRatC6XH6T1sC6nn0+lq9SqtaaYtADXAlJSVZ/3RZK1WddNJJVoBLw029evWyqmjpPg1/6csxt4Z6Aps2bZKdO3fK+vXrZf78+db8N2+2V67Q9oHqOXHiRDnnnHOs0FVDXd/beTLfm2Tam61z7gqMjJCE8fMkKCrZua2+CxlzRkv57izbYdW193MPTNQUynCcsLr2ThqgaTvmTgnv7L2Nk+P4mt7TnhkilSVlziEBYaEmGLBU8n96SQq+e80jSOYYGBAeKpFHny6tB/3VsalJ3jPmniHluzJs16quLZ1tkNtKefZG2TnnQo92VO3GPSJhqYPdRpvvRvEeSXv2T7btoamdJWHcfNs2X1rZ8fww2VtYZJtSh+tX2dZdV9zDEm1GTJOooy9xHSJFmz6S3R/UHPAJMqHR5ItMQEWTTy6vst3rjPkkly1m0QzpMG2FeQ90bi9Y+6pkL7GHQeJO+T8JTjhW8lY+LCW/rfF4bnqwfh+CE9pLm5P/IyFtj3Ser6kWvH02vbWSqyzNlbSnq6+8VVPVu/RXTpGK7H2tV/W+qmvv1lT3XNN1Cn6eLdn/fcw2JPaESyWm77W2bXVdaYrPRVnmGtk57yrblGoLtNbU5tX1RIkTnjchumNcN3ldbujvbVnmD+aerrBdK7hdgiRNWGjbpit1uZeAEBMknOI9SOgeFtNzJl00R4Lj6hag9taWMLxbb4kbfrfkrnpMin752IRIvbe11UqLsQOneW1RqvPghQACCCCAAAIIIIAAAggggAACCCCAAAIIIOBbAgSufOt5MBsE/EaAwJVvPUoNNWkoSV/ank8DRD/88IMVVtIqVtr6T9cd7fJ0nFaG0gCUhpz0pZWjNOSkYSitKKVVrE444QSrCpVuT05OtsJOGzdutK6hbfe0UpVWwNJraCBJq2Zpi0HXlwalNLjVs2dPad3a/LHRnNv1n86hR48e1lw0TKVjmuL1008/WdW1tmzZYlW00vmnp6fbLq0mWmlLP+8DBgywglZazauxX+6hEr2ehp5ih14rUUdNsAU/6jKXPZ/dIoU/fuoxtLrqR2lPm6pDpeW28d5CGY4B3kILuk9bjiWcP1uC23RzDLXeK8sLpeT3z6Vk6+cmmLFF9hZlm7BQoWktV2GOCZWQ9kdL6wH/J0Gtu1jj3auJuP8xvSJnk5RmrJbyPRtMi7ciExg4WsI6DJOg6BTbdZtqxWu4redQiTv5wTpPQcNWmfMmebS306pOKVd8atq47Qsgup9w22OmXVbV/q0aOku98sv9G3xsKePVU81z222bVeqViyUgfF8VPdcd3ip4Rfc/17Sp/IvrMCn4+WUTonncts11xTK8bKG5RrzrZueyt+9fwvmzJDSpn3NM3vdPS+4XzznXdcG1bWRVRYmU7lghGuCqyNkigVEJEprc3wTlTPU4l+CW7QRNsLLr/UukZMsvtivFnjhZYvpMsW3zFoBxDAhNNW0Ix3lvQ6hjshZPk+L13ziGWz+7qqui5Rx0iBZK0r6WXW/dYLt6+OF9JX7007ZtdV1pis9F0aYPTaDwH7Yp1RRq81bFy3awy0p4934Sf+osly3eFxv8e+ulapde2f33jtVK8IVTbW1IdZwGGasqXX7wmW3tpy6TVkFhutv2cg/w6s7EiS+bAORRtnHVrVTkbTfV7c627Q7teLgknPWac1upaXlavmut+dn2q/m6m99pSX3N76ShtvabzsEsIIAAAggggAACCCCAAAIIIIAAAggggAACCPisAIErn300TAyB5i3gLXClbdgGDhzYvG+smc0+Pz/fas23du1a0WWtOPXZZ59ZVau0ZZ9Wm9KKVBrI0iBVRESEFcxq166dnHrqqaLvnTt3tipMabBIq05pyEirYmn46LfffhOtfKWBKv2nVaC0+pNeT18a2tLza0hKWxVqkErbDDqqY/Xp00f69evnDHbpuXWcVsXSYw/FS6//66+/yuLFi62glbYh1JaK7q/ExEQ5//zz5bTTTpOuXbtawSv3MY2zXiXbHu1f7akDggMl9LDe5g+4vU2Ao58ERiRoGsv8wTnQtEmqMMGlXSbAtMsKIJX+vkLKs3Z4/HFaT64BruTJH3sN7qS/fLJU5ObZ5lBdhabclQ9K3op5trG6Etwu0bTxe91cJ9Jcv0SKfn3P/FskZRm/mWpVpR7j3TdYVayu2hcU8tYOMX7sPRLe6WT3w3xiPeerf0v+6vc95tJ2zL/+aA/pscu5oTz7N8n+7DYp3farc5vrQm33veP5oaZiVLHrIdLhuhWHNORjm4zbird2kQnnPm6qIg1wGyniLczS9vR/S0SXU21jc7+5X/K+qb6qV/wZd9VYcS3zzfNMWGqL7Zzu7TeLflskuxfebhsTFBsjyZcssW3ztZU9S26WwrWf2aYVfsTxEv+nJ23baqpYlzL5I6+BOMcJvAXeUiYvkMDIJMcQn3nXn03bnzzRNp+g1rGSfLFnQNU2qJqVpvhceK3CZubj3ra0dMc3smfJbSbUmu0xW62aGN79JClYs8i2z2rTOXV/WM6202WlMb63ac+dKJVFJS5XEQlJSDH39YQExbSX0vSVkvXeDR7Vo8I7Hy2VGnDcttF2bJuRN+4LKNu2iqQ9c4LH76B2Zz9oBaLchnpfrdor2x5z+39dk3dPnfKZ9fvO+0FsRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGiOAgSumuNTY84INAMBAleH7iFpqz4NDWkA6umnn7bCVGlpaVZlKQ1NVZgqQRpm0gpVWllK2/9pCErDTvpPK1rpuu7TgJUGjrQqlh6nVZ40qPXiiy9aLQB///13K3ilVZ00tKVjNEylbQFTU1Pl6KOPts7laDGooS69toaVtHKVLjdFRai6PA2t8vXdd9/J8uXL5Y033rDuVSt+ub/at28v2kLw+uuvly5dulj36D6msde9tWtq6Gu2O+dhCWt/gtfT7nzjHClL32bbF3/m3RJ+2Ejbtj1LbjLhjS9s23QlvOuxptrPv6Vo3RtStP5DU+XDtDK0Fx/xOMZ9Q1B0lCRfttTanPnOBI8/pgdEhJlwiwl+mECXr732FmXKjufGeJ2WVpCJOf46ZzUVrfZVvmedCcaZdpwbFnrcp+tJIo8dJXHD7nLd5LHszSph/FMSmnicx1hf2OBeDUnnFDvkEok5bpptensLM0xVmbEmVFhp25465b8eocHMdy6sNrAW0WOwtD3F3grQdkKz4q1lmbYCS570iXOot2pbutNbi0PnQT6wkLtqpuQtn2ObSWTPYab62gO2bXmrH5Pcr2bbtulKzKAJEnv8jR7bXTdoaDDjlQtcNxkXU53v6Ett23xlxb2CnlZLaj9t5QFNr6k+F2mzBprqd3s95hgYFSmtQkKlsjDfo0qhc7AJByVdNNeqtrTj+TOcmx0LcX+6RSKPOMex6vW9Mb631VVi1Al4q2Cl2wMjwyXp4sWSu/xuKfj+Q93kfIWmdDKh3zed646FtGdN4KrYHvqNGTTRfK6nO4bU+u7+mdEDNPgVf8aLtR7LAAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHmI0Dgqvk8K2aKQLMSIHDVdI9LA1Fr1qyxQk7awm/27NlWJSsNMmVkZFjVqHTMqFGjrApWjnZ9Z511lhV2SkraV1VEx2hoSqtgaRBK15csWSIa4NLAlW7XSk8aNtIAl1a50jFaAUsrPGk1LA1s6flTUlKsFoHdu3e3glkhISFNB1LPK+3evduq+rVy5UqZN2+eFQTTwJj764gjjrDu8brrrrPCaBq2OlSv4q1LTSWPPzfa5WsL7mR9dI0Ub/jWdv3ECS+YVn29nNuKt3wqWe/f4lx3Xajuj+OuY2pbdq0GVfDzS6ZF3BMeh1iVaCaaCi2BB/D5My2sDug4j1l43+At+GQbqR1A6xFCi+53trQe/DfbKbyt5Hx1p6mu9Z5tl7cAk23AIVzJ/uJ2E5SwV9kJiok2gZBFf7QDq5LiLUtkz8e3m6o05pm5vEJTO5vWdp6VrLxV+dLDtKpPymWmAlVALdX1Kitk+1ND7JXhzPPqMPWr/Z8ZMybt2aFeQy3VVYNzmXo1i+YDUWlCNLXNr5qj67K5cMM7suej/9iGRvc/z7RltP+88Ra42ld1bpk5Vj+8Nb+2P97f1uItJKmDJJ7/ds0HHaK9GXPHmPZvmbarJ1/8urOlqW1HbStN9LnI+fouyf/23dpm47nfPLq2p/3TVNrbFwj11v5UQ0wpkz2DtK4na4zvrbaGzXj1AtvnxvWa7svaWjbp4vdNlcd2Urz5Y8lacKt9iH5np3lW9/NaSauen8/M9yZJ6dZ19uuZtch6to61naCRfyfZrsUKAggggAACCCCAAAIIIIAAAggggAACCCCAQJ0ECFzViYlBCCBQXwFH4Gr69Okyc+ZM63BaCtZXsfrxGzdutCowaQhKqzHpek5OjmRmZlohqGOOOcYKTWkrvzZt2siwYcOs1n3JyclW1SqtRKXBKa2EpcEsbTeorQZ37twpP/zwg1XFynE+3aYvPY8e72gRqO0htYrV4YcfLj169LCCVZWVlVZrQh2v4a1WrWr/w7uObeqXzk1bIr7//vvW/S5atMjy0qBZdHS0VQ3MdU4aHDvzzDNl/PjxVtjKdd+hWtZgRPYn93hU9DmY+Wi7qNYnTffaZsn1vLmrHjJVcOa6bpLUq0z7wbA2zm07542VsswdzvWDWQgICZbghA6mfdRRplViXwnrNMKjclX67JFSkZPrcRkNB8SNurPGdlCVpbmirb5Kfv9CytPXSYVWNjPZFte2hR4nPsgNes30l0Z7DeTU59QaXms9Ykatz8xxzrJdP8nOuZc7Vq33ulR1sh3QhCt5a56S3GXPe1xRP6uB4RHOZ+UxwGxImjRXgtt089hVXYW4xImvmMpiR3qM97Yh+4vbTBDsI9uuxAueMZ/RPs5txVuXmGDkX53rrgsavGg99A6Pz7HrmNKdq6X4t8VSuv1bqdidbioWVVi7G7MalLd2dG1OvkGiel7kOjXTljLDVKk6x7RvK3du99a+0bnTbSHzzXNNW8atzq0HUzXKeZI6LlTk75B887kqWmfCdZVVEhhtWt2OedIEqDp7PUPOivskf+Ubtn2ugU/bjjqsNMnnorLctMYbZns+tU1Nn0H82AdsPyuLNi6Q3Yvu8Di0tu9KY3xvdRIlaV/LrrdvqDWMGtwuQdqNfckKW1mTNx7bnhjscVzCeU+Y1rv9bffnLZDpWlHRNrialcriPbLj+VO9hsO0slbcqAdNG8RO1RwtopXQin5baH4nLZfynZudLQ61OmT8mOeqPY4dCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAk0rQOCqab25GgItRoDAVcM/6p9//tlq2afVpR555BErJKWVo7Zs2WKFpzT4pC37evbsKePGjbOqTGmgSkNE2iLQ8dK2gNu3b5cdO3ZY7fO0RaBWrvroo4+cVau0WpW+ysrKrFCVVsfSSli9evWyAke6XQNYvtIO0HFvtb3rvAsLC2XFihWiAcClS5daoTCHW9u2bUVNP/nkE9F2gscee6z07dtXpk6dap1a2yz60quyrEAK1s42wQHTli8rw+OPyfWZa/jhfSVu5EM1BkAc56sszjLVe0Y7Vq2CNh2uW7V/3SxlzD3dVIXZF9az7ahlpVVggIQkdpCwriOtFoXBGoJoFVjLUeazuvsX2TnnkmrHBbeNl9D2x5lATC9pFRwhZbvWmraIa4xbmvOP2d4O7nCtS9UibwMOYltF3jbJfHOS7C0orP9ZTJYx4shB0sa0EAwIia7X8WlPDTJBjH3hHT0wtNORkjD2lXqdo6kGV5blS9rTI+r92Q4/cqDEj3rM6zSzPrhMijf9bNsXfdxYaT3k77ZtNa1UluaZQMvJtnm1HbO/MpDj2My3zpfStM2OVdu7ftbDuvS2PpNBrbuYNmZZJoT0rZTtXCd7c3O8hjX0BNp2Mv7UWbZzNeSKe4WfhHMfl9CUAR6XqChIl52vauiqQkI7HC4JZ7/mMaa6DdpWM/2F0533qBbtr/2muuF12q7tN/U7VZG72fwzbXBzf5dKE66qKNhtbPOlyvzuqzRhY6+V48z3qc0Iz2CZXrgiz7TOfWmcbQ6tT7pGoo+xBxdtA2pZaYrPRVnmGhNOurZOoSttNRh/xqOmSuExHjPfOd+0kM3YZtse1ftUaTP037ZtriuN8b11nL8k7SvJevdGr2FjDY1FD7jQa/s/b9X9Wp80xTzHyY5TW++7P7lBiv73tW1bSLKpwHZe/Sqw5X33hOR++ZLtPK4roR26mQDxsRIS38PcS7mU7jS/j9LN/+dlZzrDla7jdVlb5aZescx9M+sIIIAAAggggAACCCCAAAIIIIAAAggggAACh0iAwNUhgueyCPi7AIGrhnvC2upOQ1IaENL2fnl5eZKbmyvdunWzwkJxcXFyySWXSKdOnazA1WGHHea8uLb806pTwcHB1rHr1q2TTZs2yapVq6xqWBq6CgsLs86vLQI1UKSBLQ1YaaBK2wPq9j599ldtcZ68GS04qm1pJSsNlv3666/WP61mpW4atNLX119/LUVFRVaLRA2XXXzxxdZ+18Caz962aTdUtOlDKdr8qVQWZkllUY4JEhVKlalAoyEDDTS0CggwLc8CTUAnVFqFhEtw2y4S3vlkCTtsZJ2CVq73nvXhFCn+dbW1KbhdoiRN+MB1t+xZcqMUrq3bH4aDYmPMHAZJxBFnSWhSP9t56rOS9/3TkvtFw1b/aMzAlePetLpWwZoXTUBte7V/aNexVhgtqZNpS3WuRB5+1v72dY4T1fHd5mSCJu3OetBW1aaOp2myYbs/vt6ECpfX+Xr7AklPmvHm5ry8qipKJP3lUaZKU7G1V8MmKZcvNcvex3s5hbUp79tHJffrV5y7Ww+7SqKPvdK5rgsaoto57zypyC+wbT+YlcYOXBWsfVWylzxiTdGqPDXVhE+qaWOowTOtdhUc173et+T8HBr2mIEakpnhPEfJti9MC7ibJSA0VILi2u/b3sr8/JJK8zOt2FSG059tpaatY7kJq5h2uBWVzmMPdKGmMEvmOxdK6bZfrVNrxb3kS96XgPD4A71Uk30u9LOuAaLiX7/zOteAsBCJHXKtqWB2odf9ulGr8WW+PdEWoPVW9cz9BA39vXU9f2VJtuSsuF9KfvvKfA7KJMCEzaP7TJSoHuY+qm0hWyU73xhngrbbnKdKumiOx2dXg3s7nh1h+0zFj71Hwjud7Dyurgvewp11PdbbuJo+o97Gsw0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgcQUIXDWuL2dHoMUKOAJXgwYNsqooKQQtBev+cdBQ1bfffita1WrZsmVWFSutRFVeXi7aLrBjx45y1VVXWaEobQ/YpUsX28l1nFax+uqrr6yAli7rubRdYKj5A7ZWetIwlVaoOu+880RbD+q/iIgIOeWUU6xzaetAf3iVlJTIkiVLrKpVGljbvHlftRltEajhNHXWoJW+UlJSRNsHjh49WkaOHGn5auCMl3cBbT9WumO5RHQ9fX/rpj+GlmX+ILveutoEiPZ6HKyBhdAOR0l4t9MkvMtoE/YK9xhzoBtKdqww1U9usP2x/EDPFdX7NFPF5V8HevgBHbe3aJdpxfi9CZGUmaRDhWnNWWlCJzGmNd7hpgVVxwM6p7eDSn7/TEq2f2W1ItTqSr780sDHjmdPcVZDqm6uASFBEnuihkcuqm7I/u3GN98Ei7TcUeQR5xrj2P376rGU+dZ4E+DYbLVQTRxvbynoPI15jlZA8bcfnJsOdCEgNFgSx79q2t817jPTlmhFvy0wFXj6mQo8PQ90urUeV5Gz2XyuTaAqINg5VisY7Xp7uvdKVM5RDb8QHN9Oki5cVM2Jq6Tg55dlb3G2RPe+qt4BVa8nbcLPhQavStNXmsp+P5kgbq5pyzrMVFbrW7+fvWa+ZVk/mVarcTW2w3Pca6N8bx0nP4j3ku1fmmc5x/rctR58q9czFW9dKrsX/tX6PXKwFQCra6/o9cI1bTTBxLaneVbRq+kQ9iGAAAIIIIAAAggggAACCCCAAAIIIIAAAgg0rgCBq8b15ewItFgBAlcH9ujT09Ot6lNz584VbR24c+dO0SpVRx99tBWGOvLII0VtNRikbfDcX1q9ylG96Y033rAqYWk1LG0rqCEsfddAlZ5HA1eDBw+2QkfaRk9fjkpQ7udtjuvaTjE8PFxeeukl+fjjj63wmYbIBg4caDlodas1a9ZYrRnz8/OtINv48eOtAJo6EbQ6+KeulULKTJskrUaigZaAiHgJikgylWHiDv7kNZxBQ0u7P5ompds31TDK+y4N7QQnHiaxA2dIaHJ/74PY2uQCRb++Z57pnZ4hHBNCCE0+TKL6TpaILqc2+bzqc0EN7OR++XSdWrzZzmvuMcj8vI84aoxE9722fiEZ24max0r6K6NMW7XsJp9s3Kg/S+SR5zX5df35c9Hcv7caGjvQMKbrB6k0Y5XsWfxX0+Yyz3VznZYDwkIlrPNxEjvg5n3hxDodxSAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBphAgcNUUylwDgRYoQOCqfg89MzNTfv/9d5k9e7b89NNPVts/rTil7f20tZ8GgbQaU4cOHaw2gnp2bRWo/zQc9Oabb0pGRoZkZWXJ/PnzrYtrazwNVWmYSFsO6nnatWsnEyZMsFoI+nOoSENq8+bNsypbLV261KrqpSE1DZoVFhZaxjk5OVaYrW/fvtK5c2eZPHmyJCYmSrNoH1i/j1eLHV2Rv0Nylt8tpZtWVxty0YpBwUldTWvFk0ylrjESGJnUYr18/ca1GlLBL69Jxe6NZqpVEtppqET2GN8w1Yaa8OYL170heauelb15uaaSmWcrPG3jF9i6jakC108iOp9i2j2eqP0km3CGh+5SleXFkjZraINPQL/npqeq+diY35ul5R7n1/2pV++rdOixs4k2+Ovnwl++tw3xMdDgVe6X95n2jNu8Vn/UawRGRkhI+54Sblr9hnf5U7P7+dYQTpwDAQQQQAABBBBAAAEEEEAAAQQQQAABBBBoLgIErprLk2KeCDQzAQJXdXtgWoVJw1Zz5syRL7/8UtatW2eFfrSilSNo1b59e2ub44ylpaWibfK2bNlitcLTY95//32rOpVWqtKwkYaIevfubVVzatu2rRU0CgsLswJa/tIq0OHh+q6tEtX00UcflQ0bNljVwnT93HPPtYJnavDee+9ZhwwbNkx69OhhhdmSk5Otaliu52LZ/wQq8rZJRf42k7swFcxMsCooOrXFBFn872n6xx1pwKgie53pHllqghUREhTbWQJCov3j5g7oLqpM4GpQtWGUgOAgCYiOkcDoBFP1K8FUy2trKue1lcDwdqatabxVRS/QtLxzvkzIKjCinVk1ZcL+eOV9/7TkfvGcY9XalTDucQlNGbB/2yFe4nNxiB9AU1xeWzRmbzQVIPdIq6BQ8/uog/msJjTFlbkGAggggAACCCCAAAIIIIAAAggggAACCCCAQAMJELhqIEhOgwACdoGHH35YZs6cKYMGDZLly5dbO6dPny4zZsywD2zBazt27LAqLamVhoM0DKT/zj77bKutXVJSkmiVK8eroKBA9uzZYwWGtE2gVsJasWKFBAcHW+0Cx40bJxquiouLkyuuuMIKZUVGRoo/B6wcNo73Dz/8UBYuXCgLFiyw7nvs2LGibRq1feAnn3xitQnUQNqoUaOs1ornn3++41DeEUAAAQR8QMBqsbdslgmh7TUhlGgJST3aVPs5WcI6jWiwMFrJti+kaMP7VnvTyCPPleC47j5w50wBAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEmpMAgavm9LSYKwLNSIDAVc0PS8NWt9xyixUGWr9+vXTs2FE0MDVixAjp2rWraFDK8crOzrbaBWrbQA1arVmzRjZu3CgRERGibQMvv/xy6dOnj4wcOdLaphWwQkNDHYf7/bu2VdRQmVa1+uyzz2T16tXOe9a2iRUVFdb+qqoq0YCVVg+7+OKLrbaKLcnJicICAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMBBCRC4Oig+DkYAgeoEXANXOkarXFHhSiQnJ0d27dolf//7363qVNrKrlWrVnLvvfdKt27dJCUlxUb67bffyuuvvy4aulq7dq1oUEtbDerLEbQaOHCgVeXKdmALWvnhhx+sql+zZ8+2Kn253np4eLgVuLryyiut9oFnnnmm1XpRzXkhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwIAIErg5EjWMQQKBWAQJXnkQlJSUyb948WbRokXzzzTfSvn17mTJligwYMEC6d7e3M/r5559l06ZNMnfuXCtopZWttMVgTEyMVZ1JK1odddRRLapdoKeoyJIlS0Qrf6mp60vbKmqlL61kpW0thw0b5rqbZQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBA5YgMDVAdNxIAII1CRA4MpT591337WqVX399ddWRao33nhDDjvsMGfFKj0iLS1NFi9ebFVs0opW5eXl1pjU1FSZMGGC1TpQg1q8RD744ANZsGCBfPTRRzYObbWorQPPOOMMq2KYe9Uw22BWEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqKc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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the shape of the output matches the first value (in the shape) of the tensor we pass to the model, which is basically the number of examples we send in. It doesn’t have to be just one—this is where PyTorch really shines with parallelization! All the examples are processed at the same time, making everything faster and more efficient\n", + "\n", + "Here an image to understand it :\n", + "\n", + "![understand_batch.png](attachment:understand_batch.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 2.2 Convolutional layer \n", + "\n", + "here is the link to the [documentation](https://pytorch.org/docs/stable/nn.html#convolution-layers) of convolutional layer (all types, choose which you want) ! " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1 - Create a layer\n", + "your goal here is to create a Conv2d layer with 1 input and 1 output but with a kernel size of 3 !\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Initialize a Conv2d layer\n", + "conv_layer = ...\n", + "\n", + "# Print layer weights\n", + "print(\"Layer\", conv_layer)\n", + "print(\"-\"*60)\n", + "print(\"Weights:\", conv_layer.weight)\n", + "print(\"-\"*60)\n", + "\n", + "print(\"Weight Shape:\", conv_layer.weight.shape)" + ] + }, + { + "attachments": { + "convolution_gif.gif": { + "image/gif": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interesting, yeah? Why do we have a shape like this? Let me break it down for you:\n", + "\n", + "We have a Conv2D layer with:\n", + "\n", + "•\t***in = 1***: This means there is 1 input channel. So, our input image is likely a grayscale image, which only has one color channel *(for example an input of 3 represent the RGB scale )*.\n", + "\n", + "•\t***out = 1***: This means we have 1 output channel. This is the number of filters that will be applied to the image, and here we only have 1 filter.\n", + "\n", + "•\t***kernel size = 3***: This refers to the size of the filter, which is a 3x3 grid of numbers.\n", + "\n", + "*Now, the weight tensor shape is [1, 1, 3, 3]. Here’s what each part means:*\n", + "\n", + "•\t***The first 1***: This represents the number of output channels (filters). We only have one filter, so it’s 1.\n", + "\n", + "•\t***The second 1***: This represents the number of input channels. Since we are using a grayscale image (1 channel), it’s also 1.\n", + "\n", + "•\t***The 3 (third dimension)***: This is the height of the filter, which is 3 pixels.\n", + "\n", + "•\t***The 3 (fourth dimension)***: This is the width of the filter, also 3 pixels.\n", + "\n", + "So, in simple terms, the filter is a 3x3 matrix that will scan over the input image (which also has 1 channel), and it will produce 1 output channel after applying the filter.\n", + "\n", + "\n", + "\n", + "*We can also see a stride=(1,1) that simply represent by how many the kernel need to move in height and width*\n", + "\n", + "Let's explain it with a visualisation\n", + "\n", + "![convolution_gif.gif](attachment:convolution_gif.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step - 2 pass a tensor to this layer\n", + "\n", + "Your goal here is to reproduce the gif above ! (with random value)\n", + "\n", + "create a tensor :\n", + "\n", + "- batch_size = 1\n", + "- channel = 1 \n", + "- height = 5 \n", + "- width = 5\n", + "\n", + "pass this tensor to you layer made above " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Create a tensor and pass it through the Conv2d layer\n", + "tensor = ...\n", + "\n", + "output = ...\n", + "\n", + "print(\"Output:\", output)\n", + "print(\"-\"*80)\n", + "print(\"Output Shape:\", output.shape)\n", + "\n", + "assert list(output.shape) == [1, 1, 3, 3], \"The output of the Conv2d layer is not the right shape\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step - 3 Create a layer with padding \n", + "\n", + "Your goal is now to recreate the same layer created in step 1 but with a padding = 1\n", + "\n", + "Pass the tensor created above in the new layers\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Initialize a Conv2d layer with padding and pass a tensor through it\n", + "layers_with_padding = ...\n", + "\n", + "new_tensor = ...\n", + "\n", + "output = layers_with_padding(new_tensor)\n", + "\n", + "print(\"Output with padding:\", output)\n", + "print(\"-\"*80)\n", + "print(\"Output Shape with padding:\", output.shape)\n", + "\n", + "assert list(output.shape) == [1, 1, 5, 5], \"The output of the Conv2d layer with padding is not the right shape\"" + ] + }, + { + "attachments": { + "padding_example.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the image didn't change of size !\n", + "\n", + "This is what the padding are for, he simply add a number of layer around the image *(matrix)* as the model will process and analyse also the border of the image, \n", + "here is a image to understand it \n", + "\n", + "![padding_example.png](attachment:padding_example.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4 - Chain two layers together!\n", + "\n", + "Just like you did with the linear layers, now try it with convolutional layers!\n", + "\n", + "Your goal here is to pass a 5x5 grayscale tensor through the first convolutional layer, using a kernel size of 3 and a padding of… (take a guess!). For the second layer, make sure it takes an image of the same size as the tensor, but with 3 channels (so it’s not grayscale anymore, but with a depth of 3!) and output 1 channel." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO create the two conv layer \n", + "\n", + "tensor = ...\n", + "\n", + "conv_1 = ...\n", + "conv_2 = ...\n", + "\n", + "#TODO: Create a forward function that takes a tensor as input and passes it through the two layers ~3 lines\n", + "def forward(x):\n", + " ...\n", + "\n", + "output = forward(tensor)\n", + "print(\"Input tensor:\",tensor)\n", + "print(\"-\"*70)\n", + "print(\"Output tensor:\",output)\n", + "\n", + "assert conv_1.out_channels == conv_2.in_channels, \"The dimensions of the two layers are not compatible and need to be the same, tips it's 3...\"\n", + "assert list(output.shape) == [1, 1, 5, 5], \"The output of the forward function is not the right shape\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5 - Get a 1D tensor from an image\n", + "\n", + "Imagine you want to get your *Image* as the output of your forward function coded above and flatten it into a 2D tensor.\n", + "\n", + "Why flatten a tensor, you may ask?\n", + "\n", + "It’s because the Conv2D layer outputs a 4D tensor *(batch size, channels, height, width)*, but the Linear layer expects a 2D tensor *(batch size, features)*.\n", + "\n", + "Flattening the tensor converts the 4D shape into a 2D shape, allowing it to be passed correctly to the Linear layer. This process combines all the spatial information from the convolutional layers into a single long vector of features, which can then be used by the fully connected layers for further processing.\n", + "\n", + "Take a look at the PyTorch [documentation](https://pytorch.org/docs/stable/nn.html) and find the function that can flatten the tensor for you!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO flatten the output tensor\n", + "flatten_output = ...\n", + "\n", + "print(\"Output tensor:\", output)\n", + "print(\"-\"*70)\n", + "print(\"Flatten output tensor:\",flatten_output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6 - Combine convolutional with linear Layers !\n", + "\n", + "Your goal here is to start with an 5*5 *image* and at the end ressort with an tensor with a shape of (1, 2) ! \n", + "\n", + "Add as much layer you want between, for create a real neural network ! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : create a tensor with shape (1, 1, 5, 5)\n", + "\n", + "input_tensor = ...\n", + "\n", + "#TODO : create conv(s) layer(s)\n", + "conv_1 = ...\n", + "\n", + "#TODO : create a flatten layer\n", + "flatten = ...\n", + "\n", + "#TODO : create linear(s) layer(s)\n", + "linear = ...\n", + "\n", + "#TODO: Create a forward function that takes a tensor as input and passes it through the layers ~4 lines\n", + "def forward(x):\n", + " ...\n", + "\n", + "output = forward(input_tensor)\n", + "\n", + "print(\"Input tensor:\",input_tensor)\n", + "print(\"-\"*70)\n", + "print(\"Output tensor:\",output)\n", + "\n", + "assert list(output.shape) == [1, 2], \"The output of the forward function is not the right shape\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well done! Now that you’ve made it through all of that, let’s take a little break with some simple functions that will be very useful for you as you create complex neural networks!\n", + "\n", + "___\n", + "\n", + "Yesterday, you discovered many algorithms (depending on how far you got, but don’t worry, we’re not diving into the theory *you can always go back to yesterday to refresh your memory!*)\n", + "\n", + "All the algorithms used for machine learning, especially in deep learning and neural networks, are already built into PyTorch! (Pretty exciting, right? What a funny joke, all that theory just to call it with torch… but the math is important!)\n", + "\n", + "Let’s start with the loss functions in PyTorch, and here’s a simple documentation to get you started:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step - 1 Initialise loss function \n", + "\n", + "You need to initialise 4 values ***(in tensor ! remember the first exercice of today)*** here :\n", + "\n", + "- a prediction of a linear regression model and it's actual target\n", + "- a predction of a logistic regression model and alos it's actual target\n", + "\n", + "let's start with linear regression (li-r) model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : initalise the tensor values for li-r model\n", + "lir_y_pred = ...\n", + "lir_y = ...\n", + "\n", + "#TODO : compute the mean squared error\n", + "lir_mse = ...\n", + "\n", + "output = lir_mse(...)\n", + "\n", + "print(\"MSE:\",output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and now logistic regression (lo-r) model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : initalise the tensor values for lo-r model\n", + "lor_y_pred = ...\n", + "lor_y = ...\n", + "\n", + "#TODO : compute the binary cross entropy\n", + "lor_bce = ...\n", + "\n", + "output = lor_bce(...)\n", + "\n", + "print(\"BCE:\",output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the gradient descend is already implemented in torch you just need to calcul your loss and *backward* it !\n", + "here's the [documentation](https://pytorch.org/docs/stable/generated/torch.Tensor.backward.html) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step - 2 Activations functions\n", + "\n", + "As the same of loss function, activation function are also available in torch ! let's create ....\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : initialise the tensor values for the model\n", + "model_layers_relu = ...\n", + "model_layers_softmax = ...\n", + "\n", + "#TODO : compute the relu and softmax\n", + "relu = ...\n", + "softmax = ...\n", + "\n", + "output_relu = relu(model_layers_relu)\n", + "output_softmax = softmax(model_layers_softmax)\n", + "\n", + "print(\"ReLU:\",output_relu)\n", + "print(\"-\"*70)\n", + "print(\"Softmax:\",output_softmax)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "Well done guys, you have know the base concept to create a model with torch !\n", + "dive into the vision parts to create your first linear model ! \n", + "here is the [notebook](<../2 - Vision-Models/2.1 - Minst/Minst.ipynb>)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day04/1 - Torch/images/convolution_gif.gif b/AI/Day04/1 - Torch/images/convolution_gif.gif new file mode 100644 index 0000000..a96670a Binary files /dev/null and b/AI/Day04/1 - Torch/images/convolution_gif.gif differ diff --git a/AI/Day04/1 - Torch/images/padding_example.png b/AI/Day04/1 - Torch/images/padding_example.png new file mode 100644 index 0000000..3d56bc5 Binary files /dev/null and b/AI/Day04/1 - Torch/images/padding_example.png differ diff --git a/AI/Day04/1 - Torch/images/tasse_convolutional.gif b/AI/Day04/1 - Torch/images/tasse_convolutional.gif new file mode 100644 index 0000000..985b5cc Binary files /dev/null and b/AI/Day04/1 - Torch/images/tasse_convolutional.gif differ diff --git a/AI/Day04/1 - Torch/images/torch_logo.png b/AI/Day04/1 - Torch/images/torch_logo.png new file mode 100644 index 0000000..a30307e Binary files /dev/null and b/AI/Day04/1 - Torch/images/torch_logo.png differ diff --git a/AI/Day04/1 - Torch/images/understand_batch.png b/AI/Day04/1 - Torch/images/understand_batch.png new file mode 100644 index 0000000..9312247 Binary files /dev/null and b/AI/Day04/1 - Torch/images/understand_batch.png differ diff --git a/AI/Day04/2 - Vision-Models/2.1 - Minst/Images/cnn_background.jpeg b/AI/Day04/2 - Vision-Models/2.1 - Minst/Images/cnn_background.jpeg new file mode 100644 index 0000000..4706cdf Binary files /dev/null and b/AI/Day04/2 - Vision-Models/2.1 - Minst/Images/cnn_background.jpeg differ diff --git a/AI/Day04/2 - Vision-Models/2.1 - Minst/Minst.ipynb b/AI/Day04/2 - Vision-Models/2.1 - Minst/Minst.ipynb new file mode 100644 index 0000000..38e1036 --- /dev/null +++ b/AI/Day04/2 - Vision-Models/2.1 - Minst/Minst.ipynb @@ -0,0 +1,538 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ~ PoC AI Pool 2025 ~\n", + "- ## Day 3: Deep Learning\n", + " - ### Module 2: Convolutional Neural Network\n", + "-----------\n", + "\n", + "## Minst" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well done, you've arrived here ! You now understand key concepts of neural networks and how they are trained, but you haven't really created one yet...\n", + "Don't worry this task will guide you in recreating a neural network trained to detect any handwritten digit on a 28 by 28 pixel image !\n", + "\n", + "Your will start by setup the dataset, your model and at the end, play with it ! " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#Just import the necessary libraries\n", + "\n", + "import time\n", + "import torch\n", + "import torchvision\n", + "import torchvision.transforms as transforms\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "#For the model don't forget\n", + "import torch.nn as nn\n", + "import torch.optim as optim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part - 1 Prepare the data \n", + "\n", + "before actually create a neural network we need to preparate our data that we will fit to your model,\n", + "\n", + "remember ***THE MOST important in machine learning is the quality of the data*** and not really the model....\n", + "\n", + "your goal here is to specify how we want the data, this can be process by initialise a data and transform it in a [tensor](https://pytorch.org/vision/main/generated/torchvision.transforms.ToTensor.html) and normalize it if you want. you can check the doc of transform [here](https://pytorch.org/vision/0.9/transforms.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: define the transforms compose\n", + "transform = ...\n", + "\n", + "train_set = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n", + "eval_set = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n", + "\n", + "print(f\"Len train dataset : {len(train_set)}\")\n", + "print(f\"Len test dataset : {len(eval_set)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will say why whe created two dataset ? \n", + "\n", + "It's because one will be for the training of the model and the other for evaluate this one by passing data he never seen, to see if the model didn't overfit the data.\n", + "\n", + "To understand what's inside this code you can try below to visualise some of the examples !\n", + "\n", + "***Don't hesitate to change the NUMBER_OF_ELEMENTS enum to see mutliples examples or no***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualisation of some element of the dataset you can change the number if you want\n", + "NUMBER_OF_ELEMENTS = 4\n", + "\n", + "def imshow(img):\n", + " # img = img * 0.5 + 0.5 # Denormalisation if you have normalised the data\n", + " npimg = img.numpy()\n", + " plt.imshow(np.transpose(npimg, (1, 2, 0)), cmap='gray')\n", + " plt.axis('off')\n", + " plt.show()\n", + "\n", + "train_loader_vis = torch.utils.data.DataLoader(train_set, batch_size=NUMBER_OF_ELEMENTS, shuffle=True)\n", + "\n", + "# Random image \n", + "dataiter = iter(train_loader_vis)\n", + "images, labels = next(dataiter)\n", + "\n", + "imshow(torchvision.utils.make_grid(images))\n", + "print('Labels :', ' '.join(f'{labels[j].item()};' for j in range(NUMBER_OF_ELEMENTS)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also look at different attributes like the number of images in the dataset, the size of each image or the label of an image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image, label = train_set[0]\n", + "\n", + "print(\"image :\", image) # pixels value if you want to see the matrix\n", + "print(\"-\"*60)\n", + "print(\"image shape :\", image.shape) # pixels value\n", + "print(\"label :\", label) # Number represented in the image \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, we have images **28 pixels high and 28 pixels wide**, with **one channel** (grayscale !).\n", + "\n", + "These images represent a number from 0 to 9, we have **10 different labels** (or 10 different possible output).\\\n", + "The first picture represents a 5, therefore its label is 5." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Batch-Size\n", + "\n", + "Did you remember when we talk about batch and parallelization of multiple example with torch ? This is very important here !\n", + "\n", + "**60,000** is a lot of images to process one by one, to make it easier for our model to process this data while training we are going to use ``batch_size``.\n", + "\n", + "for one who forget , ``batch_size`` is a hyperparameter that defines the number of samples to work through before updating the internal model parameters. In other words, before calculating the error and apply backpropagation after each image, if our batch size is 64 we will go through 64 images before doing it. **This improves the learning of our AI** by **applying the backpropagation on the error average.**\n", + "\n", + "As in the previous notebook we will use a [**``dataloader``**](https://pytorch.org/docs/stable/data.html), this time we don't need to redefine a ``Dataset`` class since we are using a ``builtin`` dataset in ``torchvision``.\n", + "\n", + "Remember to specify that you use the ``train_set`` and you want a ``batch_size`` of ``64`` and also ``shuffle`` it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : Define the batch size\n", + "BATCH_SIZE = ...\n", + "\n", + "train_loader = ...\n", + "\n", + "assert len(train_loader) == 938, \"Your train loader is not well implemented, remember that the batch size is 64\"\n", + "\n", + "batch = next(iter(train_loader)) # obtain the first batch\n", + "images, labels = batch\n", + "print(\"image shape :\", images.shape)\n", + "print(\"labels shape :\", labels.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have `938` lots containing `64` images each (and their equivalent labels).\\\n", + "This will **drastically decrease our training time** because with one backward propagation, 64 images are processed.\n", + "\n", + "\n", + "> Pytorch is built to be used with batch, it is thus quite simple to implement it in our code. \n", + "\n", + "*you can try after to change your batch and see the difference in the learning (remove the assert for test it)* !" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Also load the test set with the same batch_size...\n", + "\n", + "eval_loader = ...\n", + "\n", + "assert len(eval_loader) == 157, \"Your eval loader is not well implemented\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Model!\n", + "\n", + "And your moment has arrived!\n", + "\n", + "I’m sure you’ve been eagerly anticipating this step, and now you’re ready to build your very first real neural network, complete with a more complex architecture.\n", + "\n", + "A quick tip for working with PyTorch: today’s task is a classification problem, as we’ve defined specific output labels. For this, we’ll be using the **[cross-entropy](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)** loss function. (Remember, yesterday you used the **[binary cross-entropy](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html)** loss with logistic regression, since the output was restricted to just 0 or 1.)\n", + "\n", + "*Don't hesistate to jump at the end of the torch introduction as helping you for initialize the model and train it !*\n", + "\n", + "IF you encounter difficulties to create your model, at the end of this notebook there is a pseudo code of the architecture as to help you to create the model, but try to do it alone ! (with everything you see before)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : Define the learning rate\n", + "LEARNING_RATE = ...\n", + "\n", + "\n", + "class MNISTModel(nn.Module):\n", + " def __init__(self):\n", + " super(MNISTModel, self).__init__()\n", + " self.flatten = ...\n", + " self.fc1 = ... \n", + " #TODO : add other layers if you want\n", + " ...\n", + "\n", + " self.loss = ... # Loss function cross entropy\n", + " self.optimizer = optim.Adam(self.parameters(), lr=LEARNING_RATE) # Optimizer Adam\n", + " #TODO : add other optimizers if you want\n", + " self.relu = ... # Activation function\n", + " ...\n", + " \n", + " # Device choice \n", + " if torch.cuda.is_available():\n", + " self.device = torch.device('cuda')\n", + " elif torch.backends.mps.is_available():\n", + " self.device = torch.device('mps')\n", + " else:\n", + " self.device = torch.device('cpu')\n", + " print(f\"Device : {self.device}\")\n", + " self.to(self.device)\n", + "\n", + " def forward(self, x):\n", + " #TODO : Define the forward pass\n", + " x = ... \n", + " ...\n", + " return ...\n", + "\n", + "\n", + " def train_model(self, epochs, train_loader):\n", + " self.train() # Training mode\n", + "\n", + " for epoch in range(epochs):\n", + " start_time = time.time() # Start time of the epoch\n", + " running_loss = 0.0\n", + " total_batches = 0\n", + "\n", + " for i, data in enumerate(train_loader): # Enumerate the data, all the dataset\n", + " inputs, labels = data\n", + " inputs, labels = inputs.to(self.device), labels.to(self.device)\n", + " \n", + " #TODO Compute the training part ~ 5 lines\n", + " ...\n", + " ###################################\n", + "\n", + " running_loss += loss.item()\n", + " total_batches += 1 # just help for print \n", + "\n", + " # print every 8 mini-batches\n", + " if (i + 1) % 8 == 0 or (i + 1) == len(train_loader):\n", + " print(f\"\\rEpochs {epoch + 1}/{epochs} | Lot {i + 1}/{len(train_loader)} | Loss : {loss.item():.4f}\", end='')\n", + "\n", + " \n", + " avg_loss = running_loss / len(train_loader)\n", + " epoch_time = time.time() - start_time\n", + "\n", + " print(\"\\n\")\n", + " print(\"-\" * 60)\n", + " print(f\"Epochs {epoch + 1}/{epochs} finish | Average Loss : {avg_loss:.4f} | Time : {epoch_time:.2f} seconds\")\n", + " print(\"-\" * 60)\n", + "\n", + " # change the model_path if you want\n", + " model_path = \"mnist_model.pth\"\n", + " print('Training finished, saving model to :', model_path)\n", + " torch.save(self.state_dict(), model_path)\n", + "\n", + "\n", + " def eval_model(self, test_loader):\n", + " self.eval() # Evaluation mode\n", + " correct = 0\n", + " total = 0\n", + " with torch.no_grad():\n", + " for data in test_loader:\n", + " images, labels = data\n", + " images, labels = images.to(self.device), labels.to(self.device)\n", + " outputs = self(images)\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).sum().item()\n", + "\n", + " print(f'Accuracy of the model on {total} images is : {100 * correct / total:.2f}%')\n", + "\n", + " def load_weights(self, model_path):\n", + " self.load_state_dict(torch.load(model_path, weights_only=True, map_location=self.device))\n", + " self.eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Well done ! you need now to initialise your model by simple call your python class, \n", + "\n", + "It permits that if you want to restart the training with random weights, you can restart this cell. Otherwise, the training if (you restart it) will continue from the **`last loss value`** and the **`last weight`**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_model = MNISTModel()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: define number of epochs\n", + "EPOCHS = ...\n", + "\n", + "my_model.train_model(EPOCHS, train_loader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "you can now test your model by simply call the eval function !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_model.eval_model(eval_loader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you’d like to retrain and check for better results, simply re-run the training cell or initialize a new model to start fresh!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Play with your model !\n", + "\n", + "Now it's time to test your own model! **Please paste your model architecture** (*`__init__`* and *`forward`* methods) into the file [model.py](model.py), and run the following command in the terminal:\n", + "\n", + "```bash\n", + "python app.py\n", + "```\n", + "after this break, you have two option : \n", + "\n", + "- ***2.2 - Cifar*** -> try to implemente an really complex architecture called VAE-GAN for another task \n", + "\n", + "- ***3.1 - My torch*** -> try to recreate some function of torch, to really understand how this is work (it my be help you for creating a VAE-GAN architecture :))\n", + "\n", + "choose one ! *(you can do both also if you finish in advance)*\n", + "\n", + "---\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO : Define the learning rate\n", + "LEARNING_RATE = ...\n", + "\n", + "\n", + "class MNISTModel(nn.Module):\n", + " def __init__(self):\n", + " super(MNISTModel, self).__init__()\n", + " self.flatten = ... # Flatten the data\n", + " self.fc1 = ... # Fully connected layer from 28**28 to 128\n", + " self.fc2 = ... # Fully connected layer from 128 to 64\n", + " self.fc3 = ... # Fully connected layer from 64 to 10\n", + "\n", + " self.loss = ... # Loss function cross entropy\n", + " self.optimizer = optim.Adam(self.parameters(), lr=LEARNING_RATE) # Optimizer Adam\n", + " self.relu = ... # Activation function\n", + " \n", + " # Device choice \n", + " if torch.cuda.is_available():\n", + " self.device = torch.device('cuda')\n", + " elif torch.backends.mps.is_available():\n", + " self.device = torch.device('mps')\n", + " else:\n", + " self.device = torch.device('cpu')\n", + " print(f\"Device : {self.device}\")\n", + " self.to(self.device)\n", + "\n", + " def forward(self, x):\n", + "\n", + " x = ... # Flatten the data\n", + "\n", + " ... # Compute your self.fc1\n", + " ... # Activation function\n", + "\n", + " ... # Compute your self.fc2\n", + " ... # Activation function\n", + " ... # Compute your self.fc3\n", + "\n", + " return ...\n", + "\n", + "\n", + " def train_model(self, epochs, train_loader):\n", + " self.train() # Training mode\n", + "\n", + " for epoch in range(epochs):\n", + " start_time = time.time() # Start time of the epoch\n", + " running_loss = 0.0\n", + " total_batches = 0\n", + "\n", + " for i, data in enumerate(train_loader): # Enumerate the data, all the dataset\n", + " inputs, labels = data\n", + " inputs, labels = inputs.to(self.device), labels.to(self.device)\n", + " \n", + " # Gradient to zero\n", + " ...\n", + "\n", + " # Forward pass\n", + " outputs = ...\n", + "\n", + " # Loss calculation\n", + " loss = ...\n", + "\n", + " # Backward pass\n", + " ...\n", + "\n", + " # Optimisation step\n", + " ...\n", + "\n", + " running_loss += loss.item()\n", + " total_batches += 1 # just help for print \n", + "\n", + " # print every 8 mini-batches\n", + " if (i + 1) % 8 == 0 or (i + 1) == len(train_loader):\n", + " print(f\"\\rEpochs {epoch + 1}/{epochs} | Lot {i + 1}/{len(train_loader)} | Loss : {loss.item():.4f}\", end='')\n", + "\n", + " \n", + " avg_loss = running_loss / len(train_loader)\n", + " epoch_time = time.time() - start_time\n", + "\n", + " print(\"\\n\")\n", + " print(\"-\" * 60)\n", + " print(f\"Epochs {epoch + 1}/{epochs} finish | Average Loss : {avg_loss:.4f} | Time : {epoch_time:.2f} seconds\")\n", + " print(\"-\" * 60)\n", + "\n", + " # change the model_path if you want\n", + " model_path = \"mnist_model.pth\"\n", + " print('Training finished, saving model to :', model_path)\n", + " torch.save(self.state_dict(), model_path)\n", + "\n", + "\n", + " def eval_model(self, test_loader):\n", + " self.eval() # Evaluation mode\n", + " correct = 0\n", + " total = 0\n", + " with torch.no_grad():\n", + " for data in test_loader:\n", + " images, labels = data\n", + " images, labels = images.to(self.device), labels.to(self.device)\n", + " outputs = self(images)\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).sum().item()\n", + "\n", + " print(f'Accuracy of the model on {total} images is : {100 * correct / total:.2f}%')\n", + "\n", + " def load_weights(self, model_path):\n", + " self.load_state_dict(torch.load(model_path, weights_only=True, map_location=self.device))\n", + " self.eval()\n", + " \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day04/2 - Vision-Models/2.1 - Minst/app.py b/AI/Day04/2 - Vision-Models/2.1 - Minst/app.py new file mode 100644 index 0000000..3022fd0 --- /dev/null +++ b/AI/Day04/2 - Vision-Models/2.1 - Minst/app.py @@ -0,0 +1,45 @@ +from flask import Flask, request, jsonify, render_template +import numpy as np +import torch +from torchvision import transforms +from model import load_model + +model = load_model('mnist_model.pth') +app = Flask(__name__) + +transform = transforms.Compose([ + transforms.ToTensor(), + # transforms.Normalize((0.5,), (0.5,)) # optionnal normalization +]) + +@app.route('/') +def index(): + return render_template('index.html') + +@app.route('/predict', methods=['POST']) +def predict(): + try: + data = request.json['image'] + image_array = np.array(data, dtype=np.float32).reshape(28, 28) + + image_array = 1 - image_array + + image_tensor = transform(image_array).unsqueeze(0) + + with torch.no_grad(): + output = model(image_tensor) + probabilities = torch.nn.functional.softmax(output, dim=1) + _, predicted = torch.max(probabilities, 1) + predicted_digit = predicted.item() + probabilities_list = probabilities[0].cpu().numpy().tolist() + + return jsonify({'prediction': predicted_digit, 'probabilities': probabilities_list}) + + except Exception as e: + print("Erreur dans la route /predict :", e) + return jsonify({'error': 'Une erreur est survenue lors de la prédiction'}), 500 + +if __name__ == '__main__': + app.run(debug=True, port=5003) + +# Try to change the port if it's already in use \ No newline at end of file diff --git a/AI/Day04/2 - Vision-Models/2.1 - Minst/model.py b/AI/Day04/2 - Vision-Models/2.1 - Minst/model.py new file mode 100644 index 0000000..b44335b --- /dev/null +++ b/AI/Day04/2 - Vision-Models/2.1 - Minst/model.py @@ -0,0 +1,25 @@ +import os +import torch +import torch.nn as nn +# import torch.nn.functional as F +# import torch.optim as optim + +LEARNING_RATE = 0.001 + +class MNISTModel(nn.Module): + def __init__(self): + super(MNISTModel, self).__init__() + ... + + def forward(self, x): + ... + + +# change the model_path to correspond to your weights file +def load_model(model_path='mnist_model.pth'): + model = MNISTModel() + + model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'), weights_only=True)) + assert os.path.isfile(model_path), f"Model file not found: {model_path} please change the model_path parameter in the model.py file" + model.eval() + return model \ No newline at end of file diff --git a/AI/Day04/2 - Vision-Models/2.1 - Minst/templates/index.html b/AI/Day04/2 - Vision-Models/2.1 - Minst/templates/index.html new file mode 100644 index 0000000..820f32a --- /dev/null +++ b/AI/Day04/2 - Vision-Models/2.1 - Minst/templates/index.html @@ -0,0 +1,211 @@ + + + + + PoC AI Pool 2025 - Minst + + + +

Reconnaissance de chiffres MNIST

+ + +
+ + +
+ +

+
+ + + + \ No newline at end of file diff --git a/AI/Day04/2 - Vision-Models/2.2 - Cifar/Cifar.ipynb b/AI/Day04/2 - Vision-Models/2.2 - Cifar/Cifar.ipynb new file mode 100644 index 0000000..fc42b92 --- /dev/null +++ b/AI/Day04/2 - Vision-Models/2.2 - Cifar/Cifar.ipynb @@ -0,0 +1,325 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ~ PoC AI Pool 2025 ~\n", + "- ## Day 3: Deep Learning\n", + " - ### Module 2: Convolutional Neural Network\n", + "-----------\n", + "\n", + "## Cifar" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's level up the difficulty , you just setup a linear model for classification, but as you know even if you process matricess bloc, it's even a image, so convolutional layers should performed well ! \n", + "\n", + "For this task your goal is to create a convolutional model in the dataset of cifar a dataset that represent " + ] + }, + { + "attachments": { + "cnn_background.jpeg": { + 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fl81V/hWuH/Yc+LWuXfxy+Lfwtl+JZ+Lfg3wzBZXeg+J5ryK8mkjmXdIrXUf+u2tJtZmZtrRsKAPuKivzc+C+gfGT9pL4sfHvwyfi/r/g74feHvHmqQLcaVdySau7faGWO1gmkZvs9vHHHu2x7fmb7rfw+l/sg+J/H3gL9pP4p/AXxn401H4h6b4esrbWNF1vWG8y7EM21nWSRmaRv+PhV+Zj/q227VZVoA+2KK/Nz4L6B8ZP2kvix8e/DJ+L+v8Ag74feHvHmqQLcaVdySau7faGWO1gmkZvs9vHHHu2x7fmb7rfw+l/sg+J/H3gL9pP4p/AXxn401H4h6b4esrbWNF1vWG8y7EM21nWSRmaRv8Aj4VfmY/6ttu1WVaAPtiivzH+L37X3hn4i/tBePfCvjj45eIvgv4C8J3raPY6f4Nt7qPVNUvIX2z3El1DbzbI1kV1VP4l2/Kv3m6/9iv9piPWv2kdY+FGgfE/WPjR8O7rRm1jStf1+1mXUtOmjkVWt55Zo42kXb83mbfvNHt2/NQB7R+x38YfGHxV8c/H7T/FOsf2pZeFvHF7o+kRi1hhNraxySKke6NF3/Kq/M25v9qvqGvhf9iO81Kx8TftgXWj28d7q8PxB1eSytpN22adWm8tWx/CzYH5151+y/HqP7SehJrGpftTeOfC/wAb4ry6iv8Awh9uhtrewkWVl8v+yZUUTKqsvzL8qs235WTaoB+ltFFFABRRRQAUUUUAFFFFABRXyp/wVG/5MU+Jv/cM/wDTnaV4t46/YL0zxJ+zjf8AxL8U+M/Emu/GC00FvEVt4mN88EdpdRwfaIo4IV+WOFdscfyru+Xcu37tAH3J8TL/AMUWHgPWp/BNhZan4uW3P9mWuqPttWn/AIfNbcvyr95trbvl4r41+OWqftefs9+Bbz4q33xF8J+K7DSzFc6t4MsdBSG0hg3Kr+TcMvnyKv8AtMrbWZv4QtfRH7FnxC1f4o/stfDfxJr1y97q93pax3N3M26Sd45Gh8xm7s3l7m/2mrxb9u74i3nxd1Sw/Ze+H801x408VyW8+u3kcbvBo2lK6yNJOy/3tq/L/Ep2t/rF3AH1R8JPH9v8Vvhd4T8ZWtu9nD4g0u21MW7tuaHzolfYWH3tu7bu/wBmuzrnPAPgyw+HXgjQPCulKY9M0Swg062GefLjjWNc/wC18tfN/wDwUQ+J3iTwZ8L/AAr4T8Ia0/h3XfHviW08Mf2tF/rLS3m3eZJGM7t33F3L0DN8yttoA+s6+RPDHjXxDdf8FOfF/haTXtVk8MW/w8jvodFe8kayjuPtVqpmWDdtWTazLu27vmaovCf/AATe8G/DPxH4a8TeBPFXibw94u0rULa91DV57+S7GuKsitPHdxblVvMXzF+Tbt8zd82NteR/Ej4R3fxp/wCCn3iLw5H4p1TwtpL+BLa41htHl8m61C1S5g/0VZh80atJ5LMy/NtjZf4qAP0cor87dE+Dlh+xV+3b8KPD3w41HU7PwT8Q7TUYdV8P3eoSTQma3hZo5AG/u7otrNubiRd3zV65+2Z4H/Z/u9e0nxJ8fPGl/a6RHZ/ZbTwnLrNxDZ3JRnZrhLO2/fSSfvFVpFP/ACzhX/eAPraivzE/ZA8d/DvRP24h4S+AHivUdQ+Eeq+FZru50S4kvfs1tqKyNny47ra27asbb/8Apoy7qvfDb4DH9on9sP8AaY0LXfEuq2HgDTtZsrnU/DmkzNa/2xNLDNHGJpl/eeUqpLujH3mZf7tAH6W0V+f37MvgZv2Xv26vE/wa8Maxqd38ONU8JL4ktdIvrx5Vsbrz442ZewLbZP8AeVo9zNtrm5fgvcfHj/go38f/AAxfeK9X8P8Ag0WWiXes6Zo0v2ebVdun26wwtMvzLGrSMzKv3vlHoygH6S0V+e3wo+Glv+yD/wAFAtF+G3gTUNVh+HfjXw1canLoF7etPBbXcfmfvUU/7Nsi7m+b5mXdt2iuf/aj+LQ+J37XWvfDLxd4V+JfjX4aeDtMt2uvCXw5sZLj+07q4hjm869aOWORYVWVVVd33k/h+bcAfpTXyL8GfGviHUv+Chf7Qfhu817VL3w7pml6PJY6RPeSSWto0lrbtI0UTNtjLMzM21fm3fn4R+z61/8ADv8Aam8DQ/Bv4R/GD4ffC/WUuLLxTonjTSLiLS4sRs0F1DNLJNtbflm3Mv3dqn941ev/AAH/AOUm/wC0z/2CNC/9IragD7Sor8otF8MfC1/iz45079r2TxLonxQuPEE1z4c8UapfXlnp0liu1oWsJoW8mPbt/wCWnyr+7VW3blX9PPBmlWeg+D9B0uw1C41ewsbC3tbfULu4+0z3UaxqqySS/wDLRmVQzN/FndQBZ8TaHF4j8OappMp2xX9tJbs3puXbVHwFrkniLwdpGoXACXc1sn2hP7sy/LKv4OrCuiPJxXF+AM6ZqninRG+VLTUmu4FPeG5HnZ/7/NOv/Aa0j71OUf5f6/yM5aTiHwtHk+GbrTzwdO1O+tAv92MXMjRD/v20dO+Fw8vRtXtu8Ot6l/49dSSf+1KTwjiw8beNbEnIku7fUUX0SS3SP/0K3ko8CN5Os+N7b/njrZP/AH8tbeX/ANqVvUTk538pf195nGy5fuO3ooorkOg+RP26/wBg9P2xJPDWoWPimPwrrehQ3EKvPZtcx3cchjZVbEi+XtZWO5VbO/2qP9hf9g5f2PJPEuo3vidfE+t63HDbl7e0+zwW0MfzbdpZmZmZvvcfdX5a9V/aI/az+HP7LenadceO9UuLW41PzPsFhZWzTz3Hl7d+1V+VR8y8sy/eqH9nX9rn4b/tR2mqy+B9UuJbrTPKN3p2oQeTcxK/3W25O5c7l3KzfMvuu4A9wooooAKKKKAOF1fdqvxR0S15MWmWFxfv7SSMsMX/AI79o/8AHfenfDHN9p2p643LazqE10rY6wr+5h/OOGNv+BfxfebltR1i5ij+I2t2zFLyW4t9A06T+LzFVY4+jf8APxdScfL+tdL4nlk8C/D9LLRFRb2KKHTdKjkTgTNtihyq9QvDNxjarfdXp2yi7RhHraP/ALd/7cc8Ze9zDPAgl17W/EfiOdVMEtz/AGdpzIcn7LB8rMP96YzHP8SrHywC132QayfD2g23hzRrHTbQObe0hWBDK252A/iZv4mbqzd2rWWuSo7y0NIR5VYWiiipNBvauc8UeE4PEkULiaSy1G1YyWmoW/E1u3t/eU4wyt8rDrXS03jNOMnF3RLVzyjU7m/8SWX/AAjusR2+meMLNvtmmXO0m0vJI/uyRn+6yttkj+8quw+Zfmrxv4U/FGz+EHhH4s3F5bm3g0IDVodPlO2TLL5ZhZu7K6JGzf3q+n/EXh6y8Tac1neq2FZZYpoztkgkX7skbfwsvY18C/tzeFNe8J2t9d3hEkGtxw2NxqMC+XFe7Zo5FZl/hk/druX7rbdy/eZV9FV4/U8RGO/Lzf8AgPvG+WYGONzbCUKnwyqRj/4FLlPkbWtc1DxTrupa7rFwbrVNTna5uJSf4mOdv+6v3VX+GqbHmkJoJr+f5zlUlzSP9QMLhqWEoxoUY8sYhRRRWR3AyKyMrfMrV+gn/BOD4uy694X1X4e6lM0914f2T6c7tljaSE5j/wC2bf8AjsiL/DX5+Dk5r339g/WH0j9qTw7CuQupWF7avj+JVhab/wBCjWvoclryoYuMV9o/IPE3KaWZcOVqko+9S96P/bv/AAD9YKKKK/Uj+Cynf38GlWNzeXUiw21vG000jfdRVXczV+G/xL/aO+NH/BQr44nwT4LvdSsPDeo3E0OneHLa4NrbRWX8U1+Yz+8+Rdz7t+3cyxj5trfsr8e9Cv8AxT8DfiJo2kiV9U1Hw5qNlarAMyedJayIm3H8W5lr8V/+CX3xn8J/BX9ppNQ8YXtrpWl6vpE+kJqd7II4bOaSWCRHdjwqt5Jj3Haq+Z8zbaAKHxj/AGW/jl/wT7utC8Yf8JJDp8V9cG2i1vwjqU4SKbazLBNvjjY7lWRtu1lYKwr9X/2CP2m7n9qT4EWuvax5EfivTLptN1hIBsV5FVWSZY/4VkjZG/u7vM2/dr58/wCCn37Snwe8ffs0ar4W0Xx1oHibxNLf2lzp9ro92t5taOVd7eZDuRf3bSfeZc/MPvcVyH/BDnVZ5dH+MGnM3+jW8+lXEa/7Ui3at/6LSgD9SaKKKACiiigAooooAKKKKACiiigAooooAKKKKAPzM+FXxk8d6l/wVm8UeELvxlr1z4RjmvoY9Anv5WskWOz3Ltt92xfmXduVf/Qq+fP2qv2ifjb4U/bE+Kdx4N8eeJLLS/CV3Hf/ANj2+p3D6fDBGbaL5rXc0bKWkTcrLt+Zq9T+EH/KaLxX/wBfOo/+kFXPhV4O0r4h/wDBUf4++GdctVutI1bRdRsrmFlBDRyfZF6t0bow/wBqgD6e/aE+P8vjv/gnfr/xW8D6pdaDdajolvdwXOm3bLPZTNcRRzwrMu1t8beZGzLt5Vq8n1n4s+NNN/4JAR+OI/FWsnxg2n24/t9r+b7buk1dIWbz93mbvLYru3V8xjW9W/Z5+EH7T37Mfiq8LCwjj1jw9JL8v2mP7Tb+Zt/2ZITDNsX7u2b/AGq9w8V/8oPIP+vGz/8AT9FQB518H/hR+0H8Q/2VLj45aJ+0p4xGo2Nvf3q+HL+/u5o5FtWkDL5zXDKzMsfyq0W3c3/Aq9a+Ev7Ynjv41/8ABPL4u6/qmozaf438KWUlkviHTZPInn3RoyTfLt8uT5mXcn+8u2vEP2a4f2tPiB+yXZ/D34aeE9Ag+Gur/brT/hJZbyGG9aOSSRbhW3XO5V3M67lh3cV9O3P7JJ/Zb/4Jw/Fnwkt5Fr3iK80u61bVr21h2qzLGvypu+by444/4v8Aabau7bQB1/8AwSr8f+KPiR+zBLqvivxBqniXVI9eu7cX2rXkl1P5apCyr5kjM235mrxf9or4y+PdB/4KkfDnwppvjLXNP8LTy6VFPodtqEsdlMsjN5nmQK/luW3feZf7v92vR/8AgjfNFL+yXepG4Z4vE14sgB+63k27f+gsteH/ALSVhJqP/BYP4aRxkArLpE3P91N7N/6CaAO8/a7/AGX/AIw+A/D3xR+LOl/tPeNbbTbWa71q18L2s15BDbxSTMy2sci3m1VjVgqssf8AD91a86/ZC/Zz+NP7U3whsvHsv7VPxB8NxT3s1q2mreXty22Ntu7zPtqfe/3a+6P29v8Akzn4sf8AYEk/9CWvKf8AgkX/AMma6V/2F7//ANGUAfadFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV4F+3t/wAmc/Fj/sCSf+hLXvtcV8W/hppfxl+G3iDwTrc95aaVrdo1pcTWEirOqN/dZlZd3/AWoA8u/Zs8I6NYfsPeBdKtdMtrfTtQ8D2s93bQx7VmkuLFZJ2b1aRpHZm/2q4n/glRM8v7EvgtXYlY7nUUVWP3V+2zN/7NX0d4G8A6d4E+HHh/wXZPcXWkaLpVvo8DXbq00kEMKwr5jKqjdtUbiqr+Fct8E/2e9C+APwag+G/hnVNZbSIRceXf3dxGb2MzMzMyyRxqu5Wb5fl/OgD5u/bx/wCTuf2N/wDsZL7/ANGadX3RXxLrf/BLHwR4kv8ASr7V/iv8XNUvtLk83T7m+8RwTS2kmVbfEzWpaNtyr8y/3V9q+kvgl8HbX4IeD38O2fiXxL4sia6kuzf+KtQF7d7mVV2+Yqr8vy8Lj+9QB8y/8FT7mK1+HPwpn1aBJvBkfj7T5NdWUbo2gCSkrIv8SFfMz/D932r658Vf8Ih/wj9n/wAJN/Y39ifa7X7L/a3k/ZvtHmL9l8vf8vmeZ5fl7ed23b81VPin8LPDPxq8C6l4P8YaYNX0DUVVbi2Z2jYlWVlZWUqysrKrZHpXhPw6/wCCdXwz8Ba1ot9c614y8Y22g3Ud5o2leJtca4sNLmj/ANW8MEaxr8vH3t33aAOG+Ifh/Tte/wCCsvw9/tGygvvsXw9+3WwuYxJ5Uy3t7skUH+Jc/K38NW/24rWOH9p39krUFTy70eLJ7fzkba3lt9m3L7r/APZf3q+g9Q/Z98Pal+0PpfxkmvtUXxPp2hN4fhtElj+xNbtJJJuZfL3GTdM3/LTbwvy1L8Vv2f8Aw58X/GHw/wDEmtXWp2+oeCNU/tbTRZTosUkvy/LMrI25flX7u1v9qgDy/wCMf7Snjz/hecfwY+EHhTSdY8cx6QNc1DVfFV20Gm2NqZUjU+XF+8mbdIu7aV29t3zbfB/h/wD8LN/4eg+Gv+Frf8IZ/wAJR/wgU23/AIQf7V9k+zefNs837R83nbt3+zt8uvpn46fsc+Dvjt4z0jxhcav4o8F+MtLh+yw+IvCGqfYL1ofmPls21l27nblVVv8Aa28VB8M/2JvAHwo+Klh8RNIvPEN54sitLi2u9Q1fU2vZNTabbumuXkVmaRVVVXayr/s0AeQfBXw9p+o/8FSf2hNTubKC41DTtI0hrO5kjVpLfzLG1V9jfw7l+X6Vd+JFtHZ/8FVvhNdQKY57zwVexTsjbfNVftbLu9f/ANn+7X0J4Q/Z/wDD/g/45+OfitZ3uqTeIvF8FrbX9tPNG1pGkEaRx+UqxqynbGu7czU/X/gD4d8RfHbwv8WLm41KPxP4e0640y1hhlT7LJDNuDeYpTduXzGK7WX/AIFQB82aNoGm6z/wVw8RXOoWVvdzaZ8O47yykmjVmt5/tFvH5i/3W8uWRd391jVv9pe1itf+CiP7LN9CvlXk0Os20sycO0a2zbVP+z+8k/76avoTT/2ffD2m/tD6p8ZIb7VG8T6joS+H5rR5Y/sS26yRyblXy9wk3Qr/AMtNvLfLTvHX7P3hz4hfFn4e/EPU7rUYde8Evdtp0dpNGkE32iNY289WVmbbt+Xay/e+bdQB8X+IvCnxA8cf8FNPinp3hr4nr8MtYj8Oae+lXt1oFnq73Fl5MHmwQLcMpjXzmkZtn3trbq9k0L9jb4l6h8c/h/8AEnx/8eH8b3Xg6W4aztYPCFrpMjxzJsljaa3l+6391lbjcvG9q9T+OP7KHgf48appWu6qNV8PeMNITytP8VeGr1rHVLVPn+RJl3fLukZvmVsbm/vNWb8J/wBkHQPhd4utfFNz428fePNftPN+yXXjLxHLf/Z/Mj8ttsahY87dy7mVvvN/s7QDyv4D/wDKTf8AaZ/7BGhf+kVtRrvwN+L/AOy74w+IXjr4KXuheK/CniK8ufEGq+BfEUbQTR3TfPI1ncR/xN821ZNqqvy/M21q9+8Ifs/+H/B/xz8c/FazvdUm8ReL4LW2v7aeaNrSNII0jj8pVjVlO2Nd25mryXX/APgnn4O1u51aG2+IfxQ0bwzq1xJNe+E9M8UtHpNx5rFpY2haNm2t91l3fdoA9i/Z0+NFh+0P8F/DXxC0yym02z1mORhaTtueFo5nhkXdxn5o25r0yuW8A+A9B+F/g/S/CvhfTY9I0DS4vs9nZxuz+Wg9WZmZjznczFjXU0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV8OftK6FpviT/gpD+zRZatY2+pWn9natcC3uY1kUyQ2800TbW/iWSNWU9mVa+468r8X/s/+H/GHxz8DfFa8vdUh8ReEILq2sLaCaNbSRJ43jk81WjZmO2Rtu1loA+e/wDgpJbRJqf7OepBdl9bfEzTIobleGjVm3Nt/wCBRp/3zX2xXlnxu+APhz48p4PXxDcalaN4X1638QWMmmypGzXEO7bHJuVsxtu+YLtb5fvLWd+0B+zjaftBNoX2zxz448FjSPtBX/hDNXWw+0+b5f8Art0bbtvl/L/d3N/eoA+ff+CVv/Is/G3/ALKFf/8AoEdfc1fHPw7/AOCZ3gz4W+KItf0D4n/FG0ul1CLU7mAa/CsF9LHJ5n+kqtsvmBj97d/eb1r7GoA+GPgJd2v/AA82/aFh1+KFfEr6XpjaO8gDMtgsEPmeWx6bt0G5V/i3f3a+m/iJ/wAIx/ZXxB+y/wBk/wDCX/8ACNyfb/J8r7f9k2T+R5u3955e7z9u75d3mbf4q4z4/fsXfD/9oXxPpfinVLjXvDPjHTIlt7XxJ4Y1D7JfLCpf93uZXX/lo/zbd3zferT+E/7KHgj4O+H/ABNY6O2r32qeJ7VbXWfEOsag15qV8FjaNWeR/l3Krt91VX/ZoA8t/wCCVmg6ZpX7FHgm9sbGC1u9Tm1C5vp4o1VrqRb64hVpG/iKxxRrn+6q1zH/AATjvbLwd4U/aHIBg0XR/iHq8qwRjKxxxxpnaP8AdjX/AL5r6e+A3wW0T9nn4UaH8PvDl1f3ujaOZ/s82qSRyXDedPJM25o0RfvSt0XpVH4N/s9+G/gafGw0ObULuHxbrtzr19BqUscqJNN96OPbGv7v/Zbc3+01AHz74D+Pv7Rn7Svg/wD4Tj4YeE/hp4Z+HV81wli3j6/vLi7u4Y5ZIXkdbX5Y/mjbcrf99MPmry/9jv8A5RH/ABO/7Afij/0llr3LTP8Agmx8M9IutUtLLxH4+svBepytLeeBLLxLLBok2770bRxqsjL/AL0lel+Cf2VPB3gD4D+J/hLpF1qkXhXXodRgmZ5o/tFvHeKyyLE3l7V2q3y7lb/a3UAcr/wT78MaTov7HHw5jstPtreHUdN+13caQqPtE8jN5jycfMzfdy38Kqvavmn9kqFbT/gmJ8fLWLd9ntYvFUEMbszbI1sPu196/CT4aaX8Gvht4f8ABOiT3l3pWiWi2lvNfyK07Iv95lVV3f8AAVrhPAv7JXg74efBTxr8L9Jvtabw74rbUWvJLi4ja4h+2R+XIsLeXtXav3dyt/tbqAOb/wCCffhjSdF/Y4+HMdlp9tbw6jpv2u7jSFR9onkZvMeTj5mb7uW/hVV7V8/fsSrqWmf8E7PjJB4ca5i1Kxl8Sx6UsLN5sUi2n7ry938W8/8AfVfcHwk+Gml/Br4beH/BOiT3l3pWiWi2lvNfyK07Iv8AeZVVd3/AVrD+A37P/h39nXwjqHhvwxPqNzp17qdxqrDUplkkjkmKblVlRflXb8u7c3+0aAPjH9ib4RfGbxb+zN4M1HwD+0lbeEPD8kMipoVt4D0y9+xSiRvMjeZpN0jbvm3N83zfNXpUX7M1/wDs0/sgftJWmpeN28aXviXSdd1y4mi0pNMgimk0+QSbbeORkXcV/h2r91dvy11Gpf8ABPDwHFrGq3Pgvxj8Q/hTY6m/mXmj+BPEP9n2Ezhdu7yTG3b0+WvUdG/Zu8KaB8GfFXw4tbjWW0nxPb3lvqmoXWpSXV/M1zD5EknnTbvm8vaq/Lt+VfloA8C+F37P+m/tLf8ABN/4ceDNR1K40WSTSba8stUtUWWSzuY3YxyBfl3Dkqy7l+Vm+ZfvVs+DPj18Y/g18XfAfww+NumaB4kg8Wy3Fpo3jbwwzQ+dNCit/pVuyqqyNuyfL2r83yq21q9K1T9kPwZqvwN8KfC5tT8SW2leFmSXSNYsNS+y6lazrHLGsyzRqq7ts0i/c2/N92sn4YfsS+Dvh18QrLx1qfiTxr8SPFunRNDpur+OdbbUZdPRlZWWEbUXnc33lb73y0AfRlFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXyJ/wUc8GeIvHPw/+Ftv4b0HU/EM9p8QtKvbqLS7OS5a2t1iud0ziNW2xruXczDb8w9a+u6KAPn39u/wtrXjf9kj4jaL4e0u81rV7qyi8iwsIWnmm23ETMqRr8zNtVvlWuP8Ai78RfiZ8Cf2MPBMnw+8C6x4m8ezaTpukR2dpps11Ppb/AGP95cS2yqzfu/L27W+XzGXd/db6yooA/Nb9mT4zS/s8eFLoy/s4fHHxF451uQXviXxTeeF5JLrUrpvmf52+ZY1Zm2r/AMC+8zNX6EeCPETeMfCGia/JpWoaFNqVnDd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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is a Convolutional Neural Network\n", + "\n", + "A convolutional neural network is a **deep learning algorithm** that takes visual data as input.\\\n", + "Its architecture is inspired by the organization of neurons and the visual cortex in the human brain.\n", + "\n", + "![cnn_background.jpeg](attachment:cnn_background.jpeg)\n", + "\n", + "> I advise you to look at this [article](https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53) to really understand what is convolution before leading in the subject." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import torch\n", + "import torchvision\n", + "import torchvision.transforms as transforms\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import torch.nn as nn\n", + "import torch.optim as optim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part - 1 Prepare the data \n", + "\n", + "Like previous let's load our data, but know do it alone !\n", + "Your goal is to load the data of the cifar10 dataset. \n", + "\n", + "your goal here is to specify how we want the data, this can be process by initialise a data and transform it in a [tensor](https://pytorch.org/vision/main/generated/torchvision.transforms.ToTensor.html) and normalize it if you want. you can check the doc of transform [here](https://pytorch.org/vision/0.9/transforms.html).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO Implement the dataset\n", + "transform = ...\n", + "\n", + "train_set = ...\n", + "eval_set = ...\n", + "\n", + "print(f\"Len of train_set: {len(train_set)}\")\n", + "print(f\"Len of train_loader: {len(eval_set)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and know let's visualise our dataset what's inside ? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "NUMBER_OF_ELEMENTS = 5\n", + "\n", + "def imshow(img):\n", + " img = img / 2 + 0.5\n", + " npimg = img.numpy()\n", + " plt.imshow(np.transpose(npimg, (1, 2, 0)))\n", + " plt.show()\n", + "\n", + "train_loader_vis = torch.utils.data.DataLoader(train_set, batch_size=NUMBER_OF_ELEMENTS, shuffle=True, num_workers=2)\n", + "\n", + "dataiter = iter(train_loader_vis)\n", + "images, labels = next(dataiter)\n", + "\n", + "imshow(torchvision.utils.make_grid(images))\n", + "print(' '.join('%5s' % labels[j] for j in range(NUMBER_OF_ELEMENTS)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay so like you see this dataset represent some picture of random object and the goal of your model is to predict what is it on your image ! \n", + "\n", + "Firstly print the info of the dataset, like the shape of an image and all the labels present in the dataset ! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO Print all the labels present in the dataset\n", + "labels = ...\n", + "print(\"Labels present in the dataset:\", labels)\n", + "\n", + "shapes_dataset= ...\n", + "print(\"Shape of the inputes:\", shapes_dataset.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO Implement the dataloader\n", + "BATCH_SIZE =...\n", + "\n", + "train_loader = ...\n", + "\n", + "eval_loader = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Batch_Size \n", + "\n", + "Implement the batch size alone !\n", + "\n", + "Try to do the same as you did for the mnist dataset and choss whatever you want for the batch_size, thing about perf/speed" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO Implement the model\n", + "LEARNING_RATE = 0.001\n", + "\n", + "class CIFARModel(nn.Module):\n", + " def __init__(self):\n", + " super(CIFARModel, self).__init__()\n", + " #TODO Implement the model\n", + " self.conv1 = ...\n", + " #TODO Add other layers, what you want\n", + " ...\n", + " \n", + "\n", + " if torch.cuda.is_available():\n", + " self.device = torch.device('cuda')\n", + " elif torch.backends.mps.is_available():\n", + " self.device = torch.device('mps')\n", + " else:\n", + " self.device = torch.device('cpu')\n", + " print(f\"Device : {self.device}\")\n", + " self.to(self.device)\n", + "\n", + " def forward(self, x):\n", + " #TODO Implement the forward pass\n", + " ...\n", + "\n", + "\n", + " def train_model(self, epochs, train_loader):\n", + " self.train()\n", + "\n", + " for epoch in range(epochs):\n", + " start_time = time.time() # Start time of the epoch\n", + " running_loss = 0.0\n", + " total_batches = 0\n", + "\n", + " for i, data in enumerate(train_loader): # Enumerate the data, all the dataset\n", + " inputs, labels = data\n", + " inputs, labels = inputs.to(self.device), labels.to(self.device)\n", + " \n", + " #TODO Implement the training loop\n", + " ...\n", + "\n", + " ##############################################\n", + "\n", + " running_loss += loss.item()\n", + " total_batches += 1 # just help for print \n", + "\n", + " # print every 8 mini-batches\n", + " if (i + 1) % 8 == 0 or (i + 1) == len(train_loader):\n", + " print(f\"\\rEpochs {epoch + 1}/{epochs} | Lot {i + 1}/{len(train_loader)} | Loss : {loss.item():.4f}\", end='')\n", + "\n", + " \n", + " avg_loss = running_loss / len(train_loader)\n", + " epoch_time = time.time() - start_time\n", + "\n", + " print(\"\\n\")\n", + " print(\"-\" * 60)\n", + " print(f\"Epochs {epoch + 1}/{epochs} finish | Average Loss : {avg_loss:.4f} | Time : {epoch_time:.2f} seconds\")\n", + " print(\"-\" * 60)\n", + " \n", + "\n", + " # change the model_path if you want\n", + " model_path = \"cifar_model.pth\"\n", + " print('Training finished, saving model to :', model_path)\n", + " torch.save(self.state_dict(), model_path)\n", + "\n", + "\n", + " def eval_model(self, test_loader):\n", + " self.eval() # Evaluation mode\n", + " correct = 0\n", + " total = 0\n", + " with torch.no_grad():\n", + " for data in test_loader:\n", + " images, labels = data\n", + " images, labels = images.to(self.device), labels.to(self.device)\n", + " outputs = self(images)\n", + " _, predicted = torch.max(outputs.data, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).sum().item()\n", + "\n", + " print(f'Accuracy of the model on {total} images is : {100 * correct / total:.2f}%')\n", + "\n", + " def load_weights(self, model_path):\n", + " self.load_state_dict(torch.load(model_path, weights_only=True, map_location=self.device))\n", + " self.eval()\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_model = CIFARModel()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO Implement the number of epochs\n", + "EPOCHS = ...\n", + "\n", + "my_model.train_model(EPOCHS, train_loader)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_model.eval_model(eval_loader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "Bravo ! amazing you are here, you can know try to make your model better ! Or to create your torch if it's not already did :) !" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day04/2 - Vision-Models/2.2 - Cifar/Images/cnn_background.jpeg b/AI/Day04/2 - Vision-Models/2.2 - Cifar/Images/cnn_background.jpeg new file mode 100644 index 0000000..4706cdf Binary files /dev/null and b/AI/Day04/2 - Vision-Models/2.2 - Cifar/Images/cnn_background.jpeg differ diff --git a/AI/Day04/3 - MyTorch/MynnTorch.ipynb b/AI/Day04/3 - MyTorch/MynnTorch.ipynb new file mode 100644 index 0000000..a5a448a --- /dev/null +++ b/AI/Day04/3 - MyTorch/MynnTorch.ipynb @@ -0,0 +1,452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d7d966e65a955a08", + "metadata": { + "collapsed": false + }, + "source": [ + "# ~ PoC AI Pool 2025 ~\n", + "- ## Day 3: Deep Learning\n", + " - ### Module 3: My_torch\n", + "-----------\n", + "\n", + "## My_torch\n", + "\n", + "### Utils-Methode\n", + "\n", + "After having a little introduction to the PyTorch library, we will now implement some of the methods in the library, with the objective of understanding how they work, how they are implemented and finally getting a better understanding of the library.\n", + "\n", + "### Let's Start\n", + "\n", + "First things first, we will implement easy methods such as ReLU, LeakyReLU, and Sigmoid.\n", + "\n", + "Then we will implement the class MyLinear, which is a simple linear (technically, affine) transformation, and the class BatchNorm2d, which is a simple batch normalization.\n", + "\n", + "Finally, we will implement the class MyConv2d, which is a simple 2D convolution, and the class ConvTranspose2d, which is a simple 2D transposed convolution." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "eb2624afca073dab", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import torch as t\n", + "import torch.nn as nn\n", + "from typing import Tuple, Union, List\n", + "import torchvision.transforms as transforms\n", + "from PIL import Image\n", + "\n", + "\n", + "IntOrPair = Union[int, Tuple[int, int]]\n", + "Pair = Tuple[int, int]\n", + "\n", + "t1 = t.tensor([-1, -2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=t.float32)\n", + "t2 = t.tensor([10, 9, 8, 7, 6, 5, 4, 3, 2, 1], dtype=t.float32)\n", + "\n", + "def force_pair(v: IntOrPair) -> Pair:\n", + " if isinstance(v, tuple):\n", + " if len(v) != 2:\n", + " raise ValueError(v)\n", + " return int(v[0]), int(v[1])\n", + " elif isinstance(v, int):\n", + " return (v, v)\n", + " raise ValueError(v)\n", + "image = Image.new('RGB', (256, 256), (255, 255, 255))\n", + "transform = transforms.ToTensor()\n", + "tensor_image = transform(image)\n", + "tensor_image = tensor_image.unsqueeze(0)\n", + "weights = t.tensor([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]], [[1, 0, -1], [1, 0, -1], [1, 0, -1]], [[1, 0, -1], [1, 0, -1], [1, 0, -1]]]], dtype=t.float32)" + ] + }, + { + "attachments": { + "ReLU.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "118f34cc612f14e7", + "metadata": { + "collapsed": false + }, + "source": [ + "## ReLU\n", + "The fonction ReLu looks like this :\n", + "\n", + "![ReLU.png](attachment:ReLU.png)\n", + "\n", + "The principe of ReLU is to return the maximum between 0 and the input value. Why ? Because in AI, we want 3 things, the first one is to have a non-linear function, and the second one is to prevent the vanishing gradient problem(when the gradient is too small, the network doesn't learn anything), and the last one is to have more efficient computation.\n", + "\n", + "To have more information about ReLU and it's implementation, you can check this [link](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cef06308ce6aa517", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#TODO: implement a ReLU fonction\n", + "def ReLU(x : t.Tensor) -> t.Tensor:\n", + " ...\n", + "assert ReLU(t1).equal(t.tensor([0, 0, 3, 4, 5, 6, 7, 8, 9, 10])), \"Error in ReLU\"" + ] + }, + { + "attachments": { + "LeakyReLU.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "4573ff484918ae02", + "metadata": { + "collapsed": false + }, + "source": [ + "## LeakyReLU\n", + "The fonction LeakyReLU looks like this :\n", + "\n", + "![LeakyReLU.png](attachment:LeakyReLU.png)\n", + "\n", + "The principe of LeakyReLU is to return the maximum between 0 and the input value, but with a small slope for the negative values. Why ? Because here, we want to be sure that neurons won't die (a dead neuron is a neuron that always returns the same value, and so it doesn't learn anything) and we want to use the benefits of the ReLU function and at the same time, we want to learn a bit from the negative values.\n", + "\n", + "To have more information about ReLU and it's implementation, you can check this [link](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e80496989234e575", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# TODO: implement a LeakyReLU fonction\n", + "def MyLeakyReLU(x : t.Tensor, negative_slope : float = 0.01) -> t.Tensor:\n", + " ...\n", + "assert MyLeakyReLU(t1).equal(t.tensor([-0.01, -0.02, 3, 4, 5, 6, 7, 8, 9, 10])), \"Error in LeakyReLU\"" + ] + }, + { + "attachments": { + "Sigmoid.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "b10fd97b4c7192ea", + "metadata": { + "collapsed": false + }, + "source": [ + "## Sigmoid\n", + "The fonction Sigmoid looks like this (if you forget):\n", + "\n", + "![Sigmoid.png](attachment:Sigmoid.png)\n", + "\n", + "The principe of Sigmoid is to return a value between 0 and 1 to transform the input value into a probability and to stabilize the output of the network.\n", + "\n", + "To have more information about Sigmoid and it's implementation, you can check this [link](https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5b779dcb2912ac22", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#TODO: implement a Sigmoid fonction\n", + "def MySigmoid(x : t.Tensor) -> t.Tensor:\n", + " ...\n", + "assert MySigmoid(t1).round(decimals=4).equal(t.tensor([0.2689, 0.1192, 0.9526, 0.9820, 0.9933, 0.9975, 0.9991, 0.9997, 0.9999, 1.0000])), \"Error in Sigmoid\"" + ] + }, + { + "attachments": { + "Tanh.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "faa40a8c953b5aea", + "metadata": { + "collapsed": false + }, + "source": [ + "## Tanh\n", + "The fonction Tanh looks like this :\n", + "\n", + "![Tanh.png](attachment:Tanh.png)\n", + "\n", + "The principe of Tanh is to return a value between -1 and 1 it's has the same propriety as the Sigmoid function but with a range between -1 and 1.\n", + "\n", + "To have more information about Tanh and it's implementation, you can check this [link](https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ac87645ffe1392d9", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#TODO: implement a Tanh fonction\n", + "def Tanh(x : t.Tensor) -> t.Tensor:\n", + " ...\n", + "assert Tanh(t1).round(decimals=4).equal(t.tensor([-0.7616, -0.9640, 0.9951, 0.9993, 0.9999, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000])), \"Error in Tanh\"" + ] + }, + { + "cell_type": "markdown", + "id": "5994758437f4d5df", + "metadata": { + "collapsed": false + }, + "source": [ + "## MyLinear\n", + "The class MyLinear is a simple linear (technically, affine) transformation.\n", + "\n", + "It's a useful tool to modulate the simple relation between input and output characteristic.\n", + "\n", + "Here you will need a bit more help :\n", + "the in_features are the dimensions of the entry vector\n", + "\n", + "the out_features are the dimensions of the return vector.\n", + "\n", + "the bias is the b that we add in the form of y = w . x + b where w is the weight and x is the input.\n", + "\n", + "In that exercise you will just have to implement the forward method. and you have to use the einsum method from PyTorch. and to be sure that the bias is added only if it's not None." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9cd4167e85eda019", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class MyLinear(nn.Module):\n", + " def __init__(self, in_features: int, out_features: int, bias=True):\n", + " super().__init__()\n", + " # set all the parameters\n", + " \n", + " ...\n", + " \n", + " \n", + " bound = in_features**-0.5\n", + " self.weight = nn.parameter.Parameter(t.empty(..., ...).uniform_(-bound, bound))\n", + " if bias:\n", + " self.bias = nn.parameter.Parameter(t.empty(...).uniform_(-bound, bound))\n", + " else:\n", + " self.bias = None\n", + "\n", + " # The forward method is the same as the torch.nn.Linear\n", + " def forward(self, x: t.Tensor) -> t.Tensor:\n", + " pass\n", + "\n", + " def extra_repr(self) -> str:\n", + " return f\"in_features={self.in_features}, out_features={self.out_features}, bias={self.bias is not None}\"\n", + "\n", + "linear = MyLinear(3, 3)\n", + "tensor = t.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=t.float32)\n", + "t.manual_seed(0)\n", + "assert linear(tensor).round(decimals=4).equal(t.tensor([[-0.6577, -0.5797, 0.6369], [-1.1670, -2.0570, 1.8222], [-1.6764, -3.5343, 3.0075]])), \"Error in MyLinear\"" + ] + }, + { + "attachments": { + "padding_and_stride.gif": { + "image/gif": 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" + } + }, + "cell_type": "markdown", + "id": "ec69a0c2502b4800", + "metadata": { + "collapsed": false + }, + "source": [ + "## Conv2d\n", + "The class Conv2d is a convolution in 2 dimensions. It's an essential tool for an AI model to learn be able to treat images, you should already know it with the CNN model that you made this morning ^^.\n", + "\n", + "Here we will do 2 things, first of all we will work on myConv2d, who is a function that make the convolution, and then we will create the class convolution, who implement that class.\n", + "\n", + "the padding and the stride are a bit tricky, basically, the padding is the number of pixels that we add to the input image (around the image to make the calculs), and the stride is the number of pixels that we add to make the convolution.\n", + "\n", + "![padding_and_stride.gif](attachment:padding_and_stride.gif)\n", + "\n", + "Here the padding is in grey, the kernel is in green, and the image is in blue. If we only use the kernel, we will have a smaller image, and so we add some pixels around the image to have the same size of the input image for the output.\n", + " \n", + "for more info go to check this [link](https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24eaa1e65dba41e2", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def myConv2d(x : t.Tensor, weights : t.Tensor, stride : IntOrPair, padding : IntOrPair) -> t.Tensor:\n", + " # Get the stride and the padding as a pair\n", + " padding_height, padding_width = ...\n", + "\n", + " # Get the dimensions of the input x and the weights\n", + " ...\n", + " # create the output height and output width using the formula : output = (input + 2 * padding - kernel) / stride + 1 \n", + " ...\n", + " # create a tensor with the size of the output and fill it with 0 ask for some help if you need\n", + " out = ...\n", + " # create a strided tensor from the input tensor it's a way that I recommend to do it, it's not the only one\n", + " out[... , # don't replace the ...\n", + " padding_height : padding_height + ... , # replace the ... by the correct varriable\n", + " padding_width : padding_width + ... ] = x # replace the ... by the correct varriable\n", + " \n", + " # create the conv_size and the conv_stride\n", + " conv_size = ...\n", + " # hint \n", + " batch_stride, in_chanel_stride, image_height_stride, image_width_stride = out.stride()\n", + " conv_stride = ...\n", + " strided_x = t.as_strided(out, size=conv_size, stride=conv_stride)\n", + "\n", + " # and then you have to return the result of the convolution through the einsum method if you want an hint, continue to read\n", + " # the strided_x is a tensor with the shape (batch, out_height, out_width, in_channel, kernel_height, kernel_width)\n", + " # the weights is a tensor with the shape (out_channel, in_channel, kernel_height, kernel_width)\n", + " # the result is a tensor with the shape (batch, out_channel, out_height, out_width)\n", + " return ...\n", + "\n", + "assert myConv2d(tensor_image, weights, 1, 1).shape == (1, 1, 256, 256), \"Error in myConv2d\"" + ] + }, + { + "cell_type": "markdown", + "id": "75962a1136873bea", + "metadata": { + "collapsed": false + }, + "source": [ + "## extra_repr\n", + "\n", + "The extra_repr method is a method that is used to print the parameters of the class, it's a useful tool to have a better understanding of the class and to debug it.\n", + "\n", + "Ignore it if you don't understand it is not relevant for the exercise, but if you want to understand it, you can check this [link](https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.extra_repr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e09e17e9a65a10d", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def extra_repr(module, arg_names: List[str], kwarg_names: List[str]) -> str:\n", + " reprs = [repr(getattr(module, arg_name)) for arg_name in arg_names] + [\n", + " f\"{k}={getattr(module, k)}\" for k in kwarg_names\n", + " ]\n", + " return \", \".join(reprs)" + ] + }, + { + "cell_type": "markdown", + "id": "fff519a772d0256b", + "metadata": { + "collapsed": false + }, + "source": [ + "## MyConv2d\n", + "\n", + "Here you will have to implement the class MyConv2d, who is a simple 2D convolution, but ad a class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d81702d74dfaff66", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "class MyConv2d(t.nn.Module):\n", + " def __init__(\n", + " self,\n", + " in_channels: int,\n", + " out_channels: int,\n", + " kernel_size: IntOrPair,\n", + " stride: IntOrPair = 1,\n", + " padding: IntOrPair = 0,\n", + " ):\n", + " \n", + " super().__init__()\n", + " # set all the parameters\n", + "\n", + " in_features = ...\n", + " bound = in_features**-0.5\n", + " self.weight = nn.parameter.Parameter(\n", + " t.empty((out_channels, in_channels, *self.kernel_size)).uniform_(-bound, bound)\n", + " )\n", + "\n", + " # The forward method is the same as the myConv2d function\n", + " def forward(self, x: t.Tensor) -> t.Tensor:\n", + " pass\n", + " def extra_repr(self) -> str:\n", + " return extra_repr(self, [\"in_channels\", \"out_channels\"], [\"kernel_size\", \"stride\"])\n", + " \n", + "assert MyConv2d(3, 3, 3, 1, 1)(tensor_image).shape == nn.Conv2d(3, 3, 3, 1, 1)(tensor_image).shape, \"Error in MyConv2d\"" + ] + }, + { + "cell_type": "markdown", + "id": "4c1dcc45", + "metadata": {}, + "source": [ + "---\n", + "Well done guys you are a master of torch ! gg wp\n", + "\n", + "You can know try to implement a model with your version of torch :) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/AI/Day04/3 - MyTorch/assets/LeakyReLU.png b/AI/Day04/3 - MyTorch/assets/LeakyReLU.png new file mode 100644 index 0000000..210a250 Binary files /dev/null and b/AI/Day04/3 - MyTorch/assets/LeakyReLU.png differ diff --git a/AI/Day04/3 - MyTorch/assets/ReLU.png b/AI/Day04/3 - MyTorch/assets/ReLU.png new file mode 100644 index 0000000..d771ede Binary files /dev/null and b/AI/Day04/3 - MyTorch/assets/ReLU.png differ diff --git a/AI/Day04/3 - MyTorch/assets/Sigmoid.png b/AI/Day04/3 - MyTorch/assets/Sigmoid.png new file mode 100644 index 0000000..2f1b112 Binary files /dev/null and b/AI/Day04/3 - MyTorch/assets/Sigmoid.png differ diff --git a/AI/Day04/3 - MyTorch/assets/Tanh.png b/AI/Day04/3 - MyTorch/assets/Tanh.png new file mode 100644 index 0000000..3832658 Binary files /dev/null and b/AI/Day04/3 - MyTorch/assets/Tanh.png differ diff --git a/AI/Day04/3 - MyTorch/assets/padding_and_stride.gif b/AI/Day04/3 - MyTorch/assets/padding_and_stride.gif new file mode 100644 index 0000000..2ed4ab7 Binary files /dev/null and b/AI/Day04/3 - MyTorch/assets/padding_and_stride.gif differ diff --git a/AI/Day04/README.md b/AI/Day04/README.md new file mode 100644 index 0000000..89455ba --- /dev/null +++ b/AI/Day04/README.md @@ -0,0 +1,27 @@ +# ~ PoC AI Pool 2025 ~ + +- ## Day 4: Neural Networks + - ### Module 1: Torch + - **Notebook:** [`introduction_to_torch.ipynb`](<1 - Torch/Introduction_Torch.ipynb>) + - ### Module 2: Vision models + - **Notebook 2.1 :** [`mnist.ipynb`](<2 - Vision-Models/2.1 -Minst.ipynb>) + - **Notebook 2.2 :** [`cifar.ipynb`](<2 - Vision-Models/2.1 - Cifar.ipynb>) + - ### Module 3: My Torch + - **Notebook:** [`my_torch.ipynb`](<3 - MyTorch/MynnTorch.ipynb>) + +--- + +**Already the third day !!** +On today's menu, you will dive into the wonderful world of torch, by starting of use it and create advanced neural network after ! + +It's up to you to choose the order of the 2 last notebook, start by `my_torch` before do the `cifar` is a good things also ! + +> Here's a list of resources that we believe can be useful to follow along (and that we've ourselves used to learn these topics before being able to write the subjects): + +## Ressources + +[Torch in 100 seconds](https://www.youtube.com/watch?v=ORMx45xqWkA) + +[Neural Networks Mnist video](https://www.youtube.com/watch?v=aircAruvnKk&t=34s) (*3blue1brown*) + +[Convolution explained](https://www.youtube.com/watch?v=KuXjwB4LzSA) (*3blue1brown*) \ No newline at end of file diff --git a/AI/Day05/.gitignore b/AI/Day05/.gitignore new file mode 100644 index 0000000..4de10de --- /dev/null +++ b/AI/Day05/.gitignore @@ -0,0 +1,2 @@ +*/__pycache__/* +*/runs/* diff --git a/AI/Day05/1.Introduction/Q_Learning.ipynb b/AI/Day05/1.Introduction/Q_Learning.ipynb new file mode 100644 index 0000000..c68cabe --- /dev/null +++ b/AI/Day05/1.Introduction/Q_Learning.ipynb @@ -0,0 +1,509 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Value-based method: Q Learning\n", + "\n", + "In the first notebook of the day, we will learn about one of the most popular RL algorithms: Q Learning !\n", + "\n", + "**Key facts**:\n", + "- [It was first defined in 1989 by Christopher J.C.H. WATKINS](https://link.springer.com/content/pdf/10.1007/BF00992698.pdf?pdf=button)\n", + "- It uses a **temporal difference (TD)** approach\n", + "- It is an **Action Value** function\n", + "- It is **off-policy**" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### a. Temporal Difference (TD)\n", + "\n", + "There are two different learning strategies on how to train a value or policy function :\\\n", + "One of them is 'Monte Carlo', in which the agent experiences an entire episode of the environment before learning (updating its value function). This means that it stores the state, action and reward inside a memory which it unwraps at the end of each episode.\n", + "> Monte Carlo will be explained in more detail inside the `REINFORCE.ipynb` notebook.\n", + "\n", + "Q-Learning uses 'Temporal Difference', which means it learns at each time step of the environment. In other words, the agent updates its value function using the current state, action, reward and resulting state.\n", + "\n", + "![Temporal difference](./assets/fig9.svg)\n", + "> Formula for temporal difference\n", + "\n", + "Don't let the mathematical expressions scare you, all you need to understand is that we update the state's value at each time step by adding the difference between the target and the old value, multiplied by a learning rate, to our old value.\n", + "\n", + "If it helps, here is a version of this formula in pseudo code:\n", + "\n", + "```py\n", + "LR = 0.05\n", + "GAMMA = 0.99\n", + "\n", + "state_values = [...] # the list of values for each of our states\n", + "action = agent_choice(state) # choosing an action based on the state\n", + "new_state, reward = environment_step(action) # retrieving a new state and a reward from the environment\n", + "\n", + "target = reward + GAMMA * state_values[new_state] # computing the target\n", + "state_values[state] = state_values[state] + LR * (target - state_values[state]) # updating the state value \n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### b. Action value\n", + "\n", + "Q-Learning is a value-based function and there are also two different types of those as well !\n", + "> We promise, these ones are easy to differentiate !\n", + "\n", + "- State-value functions, where each state has a different value\n", + "- Action-value functions, where each (state,action) pair has a different value\n", + "\n", + "![Action and state values](./assets/fig10.svg)\n", + "\n", + "Notice how there are (state,action) pairs where the value is 0. That is because our agent never performed the actions at those states." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Perhaps it's time to take a short break from the theory and get into implementation, shall we ?\n", + "> You'll see, it'll be much easier to understand if you take it all one step at a time !\n", + "\n", + "Let's begin by importing some libraries and defining some constants..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the necessary libraries\n", + "import numpy as np\n", + "import random\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib import animation\n", + "from seaborn import heatmap\n", + "from scipy.ndimage import gaussian_filter1d\n", + "\n", + "from IPython.display import Image\n", + "from moviepy.editor import ImageSequenceClip\n", + "\n", + "# Import the Environment class from the envi module\n", + "from envi import Environment\n", + "\n", + "# Define the actions that the agent can take\n", + "ACTIONS = {'UP': 0, 'LEFT': 1, 'DOWN': 2, 'RIGHT': 3}\n", + "\n", + "# Define the size of the gridworld\n", + "MAP_SIZE = 10\n", + "\n", + "# Define the number of episodes to train for\n", + "EPISODES = 10_000\n", + "\n", + "# Define the learning rate\n", + "LR = 5e-3\n", + "\n", + "# Define the discount factor\n", + "GAMMA = 0.99" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's create an `Agent` class which will contain our action values and a method to update them !\n", + "\n", + "In Q-Learning, the table which contains our action values is called the Q-Table ! (catchy, right ?)\n", + "\n", + "In order to define this Q-Table, we simply create an array of shape `(number_of_states, number_of_actions)` and initialize all of its values to 0.\n", + "You can use `numpy`'s `zeros()` method to achieve this by defining the `self.q_table` property inside the `__init__()` method of our Agent.\n", + "\n", + "Our environment is a grid world, so you can consider each square a separate state, meaning that if our grid is of size 2 * 2, the number of states is 4.\n", + "\n", + "\n", + "| Action | State 1 | State 2 | State 3 | State 4 |\n", + "| ---------- | ------- | ------- | ------- | ------- |\n", + "| **UP** | 0.0 | 0.0 | 0.0 | 0.0 |\n", + "| **DOWN** | 0.0 | 0.0 | 0.0 | 0.0 |\n", + "| **RIGHT** | 0.0 | 0.0 | 0.0 | 0.0 |\n", + "| **LEFT** | 0.0 | 0.0 | 0.0 | 0.0 |\n", + "> This is what a fresh Q-Table of a 4 * 4 gridworld environment with 4 actions should look like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class Agent:\n", + " \"\"\"\n", + " This class defines our Agent which will interact with the environment and update its Q Table\n", + " \"\"\"\n", + " \n", + " def __init__(self):\n", + " # We initialize a value called `epsilon` (we will soon learn more about it)\n", + " self.epsilon = 1.0\n", + "\n", + " # Initialize the Q Table for the agent with zeros\n", + " self.q_table = None\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, that was easy. Let's implement the Q-Function now !\\\n", + "Remember the TD formula ? Well, the Q-Function is the same as that one, except we are updating the action-value, not the state-value:\n", + "\n", + "Knowing this, let's update our temporal difference formula using Action values instead:\n", + "\n", + "![Q-Learning formula](./assets/fig11.svg)\n", + "\n", + "Now, define a new method which implements this formula in python code !\n", + "\n", + "> - The `update_q_table()` method doesn't need to return anything, you must update the q_table directly inside the method.\n", + "> - Refer to the `Agent` class above if you don't remember the contents of `self` and how they can be useful." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def update_q_table(self, new_state, state, action, reward):\n", + " \"\"\"\n", + " This method updates the Q Table\n", + " \"\"\"\n", + " pass\n", + "\n", + "Agent.update_q_table = update_q_table" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## c. Off-policy\n", + "\n", + "There is one more useful concept you need to understand before we continue !\n", + "\n", + "Because Q-Learning is off-policy, we do not know which action to choose for any given state.\\\n", + "All we have is our Q-Table, which contains valuable information which our agent will need in order to form an opinion.\n", + "\n", + "But how should the agent form an opinion ?\n", + "\n", + "An easy answer is to simply pick the action with the highest value.\\\n", + "This is called a **greedy policy**.\n", + "\n", + "There is one flaw with this policy, though. While it is a good idea to pick the action that we believe is optimal, we do not have access to an optimal policy. Therefore, while a greedy policy works once our agent is well trained (because that means our estimated policy will be close to the optimal policy), it will not work quite as well if the agent is barely discovering the environment.\n", + "\n", + "Imagine this scenario:\n", + "\n", + "![Greedy policy flaw](./assets/fig12.svg)\n", + "\n", + "Our agent has two choices: either left or right ! \n", + "\n", + "If he chooses left, he receives +10 reward !!!\\\n", + "On the other hand, if he chooses right, he receives a measly +1 reward...\n", + "\n", + "Alas, robot boy goes to the right on his first attempt, while both action-values are 0.\\\n", + "Because of this, he believes going right is the best choice, despite never having attempted to go left !\n", + "\n", + "It is a bit like deciding you hate sitcoms because you've only ever seen 'Big Bang Theory' and you hated it.\\\n", + "But because of your **greedy policy**, you miss out on a show like 'Seinfeld' ! What a bummer !\n", + "\n", + "Thankfully, there's another policy you can try: **Epsilon-Greedy policy** !\n", + "\n", + "With the epsilon greedy policy, you start by picking actions at random before gradually choosing the actions you value !\n", + "\n", + "```py\n", + "epsilon = 1.0\n", + "\n", + "for i in range(1000):\n", + " use_greedy_policy = random.random() > epsilon # use greedy policy with a probability of epsilon\n", + " if use_greedy_policy: # if epsilon is high, this will happen more often\n", + " action = greedy_action(state)\n", + " else: # if epsilon is low, this will happen more often instead\n", + " action = random_action()\n", + "\n", + " epsilon = max(epsilon * 0.995, 0.05) # decaying epsilon so that we gain confidence in our Q-Table (we tend to keep a small probability of random policy during training so we don't go below 0.05)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def epsilon_greedy_policy(self, state):\n", + " \"\"\"\n", + " This method is an implementation of the epsilon greedy policy\n", + " \"\"\"\n", + " pass\n", + "\n", + "Agent.epsilon_greedy_policy = epsilon_greedy_policy" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Q-Learning Algorithm\n", + "\n", + "Well, with that out of the way, we've defined a nice Agent class. It will come in handy for the next part, which is training the agent to solve our gridworld environment !\n", + "\n", + "Let's start by initializing our Agent and Environment instances, as well as some lists we will use to store our rewards throughout the training for plotting purposes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an environment and an agent\n", + "from collections import deque\n", + "\n", + "env = Environment(MAP_SIZE, ACTIONS)\n", + "agent = Agent()\n", + "\n", + "# Initialize empty lists for rewards and losses\n", + "recent_rewards = deque(maxlen=1_000)\n", + "train_rewards = []" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, your task is to implement the following algorithm (figure taken from [Sutton and Bartol's 'Reinforcement Learning: an Introduction'](http://incompleteideas.net/book/RLbook2020.pdf))\n", + "\n", + "![Q-Learning algorithm](./assets/fig8.svg)\n", + "\n", + "You've already implemented most of the algorithm inside the `Agent` class. Try to understand which lines correspond to which methods.\n", + "\n", + "The initialization is covered by `agent = Agent()` which we declared above.\n", + "The epsilon greedy policy is a method inside `Agent` and so is the penultimate line of second loop: updating the q-table !\n", + "\n", + "The Environment class has two methods you should know about:\n", + "\n", + "- `env.reset()` resets the environment and returns a state\n", + "- `env.step()` updates the environment by taking an action as argument and returns a tuple containing `new_state, reward, done`. The latter is a boolean which tells us whether the episode is terminated or not.\n", + "\n", + "With this info, see if you can fill in the blanks and build your Q-Learning algorithm :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Iterate over the number of episodes\n", + "for episode in range(EPISODES):\n", + " # Reset the environment to get the initial state\n", + " state = env.reset()\n", + "\n", + " # Initialize empty lists for rewards and losses in this episode\n", + " episode_reward = []\n", + "\n", + " # Iterate over the time steps in the episode\n", + " for i in range(1000):\n", + " action = agent.epsilon_greedy_policy(state)\n", + "\n", + " # Interact with the environment to get the new state, reward, and done flag \n", + "\n", + " # Set the new state as the current state\n", + "\n", + " # If the episode is done, break out of the loop\n", + " \n", + " # Log the rewards and losses for this episode\n", + " train_rewards.append(np.sum(episode_reward))\n", + " recent_rewards.append(train_rewards[-1])\n", + "\n", + " # Print a table of information about the episode every 5,000 episodes\n", + " if episode % 1_000 == 0:\n", + " print(f\"Episode {episode:>6}: \\tR:{np.mean(recent_rewards):>6.3f}\\t Epsilon:{agent.epsilon:>6.3f}\\t State:{state:>6}\")\n", + "\n", + "# Reset the environment to get the initial state\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "# plotting rewards\n", + "ax.plot(gaussian_filter1d(train_rewards, sigma=10))\n", + "ax.set_title('Rewards')\n", + "# show figure\n", + "fig.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If all went well, your rewards should increase before reaching a treshold.\n", + "> This graph depends on the parameters you set at the beginning of the notebook.\\\n", + "> You can try changing the MAP_SIZE for example for very different results.\\\n", + "> It is advised to stay below 30 for the MAP_SIZE, otherwise your agent might find that it is a better idea to kill itself rather than reach its goal ! " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check out our Q-table and observe our estimated policy as well as the values for each action-state pair !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract the optimal actions from the Q-table\n", + "best_actions = [np.argmax(x) if np.mean(x) != x[0] else -1 for x in agent.q_table]\n", + "\n", + "# Initialize a matrix for the policy\n", + "policy = np.zeros((MAP_SIZE ** 2, len(ACTIONS)))\n", + "\n", + "# Fill in the policy matrix\n", + "for y in range(MAP_SIZE ** 2):\n", + " for x in range(MAP_SIZE):\n", + " if x == best_actions[y]:\n", + " policy[y][x] = 1\n", + "\n", + "# Create a figure with two subplots\n", + "fig, ax = plt.subplots(1,2)\n", + "\n", + "# Plot the policy matrix as a heatmap\n", + "heatmap(policy, ax=ax[0], xticklabels=ACTIONS, cbar=False)\n", + "\n", + "# Plot the Q-table as a heatmap\n", + "heatmap(agent.q_table, ax=ax[1], xticklabels=ACTIONS, annot=MAP_SIZE<6)\n", + "\n", + "# Show the figure\n", + "fig.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This code extracts the optimal actions from the Q-table and uses them to create a matrix representing the policy. It then plots the policy matrix and the Q-table as heatmaps. The policy matrix shows which actions are considered optimal in which states, while the Q-table shows the values of the actions in each state." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's record a video of our agent solving the grid world !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "frames = []\n", + "\n", + "# Iterate over the time steps in the episode\n", + "for i in range(1000):\n", + " # Add the current state of the environment to the list of frames\n", + " frames.append(np.array(env.graphic()))\n", + "\n", + " # Choose the greedy action for the current state\n", + " action = agent.epsilon_greedy_policy(state)\n", + "\n", + " # Interact with the environment to get the new state, reward, and done flag\n", + " new_state, reward, done = env.step(action)\n", + "\n", + " # Set the new state as the current state\n", + " state = new_state\n", + "\n", + " # If the episode is done, reset the environment and break out of the loop\n", + " if done is True:\n", + " frames.append(np.array(env.graphic()))\n", + " state = env.reset()\n", + " break\n", + "\n", + "clip = ImageSequenceClip(list(frames), fps=20)\n", + "clip.resize(width=300)\n", + "clip.write_gif('output.gif', fps=20)\n", + "Image('output.gif', width=300)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "Awesome ! You've managed to implement the Q-Learning algorithm in python !\n", + "\n", + "Isn't it cool to see the agent go from not knowing anything to seamlessly running through the maze while avoiding all obstacles ?\n", + "\n", + "What happens if the maze is bigger, though ? Increase the value of the `MAP_SIZE` constant at the top of the notebook to see the changes. Beware though; this environment is not suited for large sizes, so stay below 30 if you want good results. Also, the larger the size, the long it will take your agent to learn. You can also change the episode count and length, if you want !\n", + "\n", + "Hopefully this notebook was fun. We decided to spare you the creation of the environment because that doesn't teach you anything about AI and it would be a little time consuming for what it's worth.\n", + "\n", + "In the next notebook, `REINFORCE.ipynb`, we'll show you a great tool that is used in RL to easily deal with pre-made environments which are tailor-made for RL ! We will also be implementing a policy-based, on-policy, monte carlo algorithm !\\\n", + "Basically the opposite to Q-Learning !" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.8 ('pool')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "6b483bbea0ef867292651300ca303e9b91f9a0c7db919f54df8d16a1790f2d11" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day05/1.Introduction/README.md b/AI/Day05/1.Introduction/README.md new file mode 100644 index 0000000..337ff2a --- /dev/null +++ b/AI/Day05/1.Introduction/README.md @@ -0,0 +1,80 @@ +![The Machine Learning Trinity](./assets/fig1.svg) + +So far, this Pool has covered supervised learning.\ +There are two other major fields inside machine learning: **Unsupervised** and **Reinforcement learning** ! + +In this fifth day, we will be exploring the various algorithms and libraries used in the latter. + +# Reinforcement Learning: The Basics + +Reinforcement Learning is perhaps the most appealing field in machine learning: teaching an artificial intelligence to play a video game or building a self-driving car can't possibly sound lame to anyone ! + +But a lot of math is involved behind all of this fun, so let's quickly get into the theory (don't skip this part, you **will** regret it) : + +# 1. Introduction + +![Reinforcement Learning Model](assets/fig2.svg) +> a basic representation of a reinforcement learning model: the agent sends an 'action' to the environment which returns a 'state' and a 'reward' to the agent. + +The key concepts in reinforcement learning are the following: +- **The Agent**, which takes an action based on the state of the environment. The agent is synonymous with the `Player` in a video game, for example, Mario in 'Super Mario Bros.`\ +In certain implementations of RL, this Agent can have a Memory of the previous events within the environment. +- **an action**, which is taken by the Agent within the environment. For example, a mapping of all the possible buttons on a keyboard, gamepad or simply a list of defined possible actions within the environment.\ +In a grid world problem, like 'Sokoban', the actions could simply be 'left', 'right', 'up', 'down'. +- **The Environment**, which receives an action from the agent and returns a state and reward based on the given action. +- a **state**, which represents all the information regarding the environment. It can also be a simple observation of itself.\ +For example, in a game of chess, the player always receives the full state of the game: there is no **hidden** information from the agent.\ +On the contrary, in a game of 'Super Mario Bros.', the player only receives an observation of the state: a grid of pixels the size of the screen. The player can not, at all times, see every detail of the state. + +![Example of observation](assets/fig3.svg) +> Example of an observation + +![Example of state](assets/fig4.svg) +> Example of a state + +- **a reward**, which is a value given by the environment to 'rate' the action the player has taken. This value can be anything but most importantly, a **negative** reward means that the action was probably not a good idea for the current state and a **positive** reward means that the action was of high value ! + +| Action | Reward | +| -------------------- | -------- | +| Taking a pawn | +1 | +| Losing a pawn | -1 | +| Taking a rook | +5 | +| Losing a rook | -5 | +| Castling | +0 | +| Winning by checkmate | **+100** | +| Losing by checkmate | **-100** | +> Possible rewards based on different chess situations + +### As you may have guessed, the goal of Reinforcement Learning is to build an AI which develops an optimal policy for solving a certain environment by attempting to maximise its rewards ! + +# 2. Two main approaches + +There are two main approaches to solving this problem and finding this policy:\ +**Keep in mind** that both methods are just as good as the other and can provide better results in different situations. Most methods follow up on either one of these approaches, though, so it is important to learn both ! + +## a. Direct approach (Policy-based) + +The first approach is to directly learn the policy function which will indicate the best action to take at each state of the enviornment. + +![Policy-based representation](./assets/fig6.svg) +> The arrows represent the optimal actions for each state, the red diamonds are obstacles (negative reward) and the green circle is the goal (positive reward). The blue robot is our agent. + +## b. Indirect approach (Value-based) + +The second approach is to indirectly learn the policy function by first defining a value for each state and picking the action that leads to the best state at each step ! + +![Value-based representation](./assets/fig7.svg) + +# 3. It's up to you ! + +Now that you know the basic concepts of RL, we invite you to start implementing two basic algorithms: +- Q Learning, which is a value-based method +- and REINFORCE algorithm, which is a policy-based method + +>While there is no particular obligation to learn value-based methods before policy-based methods, we encourage you to follow this order today because we will begin using a very important python library inside the `REINFORCE.ipynb` notebook ! + +**STEPS:** +- Follow the `Q_Learning.ipynb` notebook to get started with this day's first task !\ +You will learn the implementation of the Q Learning algorithm which is a value-based approach to solve a custom made grid world environment ! +- Follow the `REINFORCE.ipynb` notebook to implement a policy based approach, the REINFORCE algorithm in order to solve an OpenAI Gym environment, `Cartpole` ! +- Use your favorite method to solve the `Cartpole` environment and share your results ! \ No newline at end of file diff --git a/AI/Day05/1.Introduction/REINFORCE.ipynb b/AI/Day05/1.Introduction/REINFORCE.ipynb new file mode 100644 index 0000000..c061e6f --- /dev/null +++ b/AI/Day05/1.Introduction/REINFORCE.ipynb @@ -0,0 +1,673 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Policy-based method: REINFORCE\n", + "\n", + "Now that we've implemented a value-based algorithm, it's only right that we should try out a policy-based one as well, right ? So let's learn about REINFORCE !\n", + "\n", + "**Key facts**:\n", + "- It was first defined in ['Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning' by Ronald J. WILLIAMS in 1992](https://link.springer.com/content/pdf/10.1007/BF00992696.pdf?pdf=button).\n", + "- It uses a **monte carlo** method\n", + "\n", + "### Monte Carlo\n", + "\n", + "As explained in the previous notebook, you can think of Monte Carlo as a method in which our agent learns after each episode instead of doing so at each time step like Temporal Difference.\n", + "\n", + "This implies that there is no need to estimate the target: we can compute the episodic reward for each timestep using the memory batch:\n", + "\n", + "![Monte Carlo formula](./assets/fig13.svg)\n", + "\n", + "Let's begin by implementing this formula !\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But before we begin, let's import some modules and define some constants...\\\n", + "Notice this time we're using pytorch because it will make it easier for us to deal with optimization since pytorch has a built in 'Adam' optimizer which will improve our `REINFORCE` algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import numpy as np\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from scipy.ndimage import gaussian_filter1d\n", + "\n", + "# Set the learning rate and discount factor\n", + "lr = 1e-3\n", + "gamma = 0.995\n", + "\n", + "# Set the number of episodes to run\n", + "episodes = 300" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OpenAI Gym\n", + "\n", + "From now on, we will be using a popular RL framework called OpenAI Gym !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import gym\n", + "\n", + "# Set the environment to use\n", + "env_name = 'CartPole-v1'\n", + "\n", + "# Create the environment\n", + "env = gym.make(env_name, render_mode=\"rgb_array\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": "We will `make` our gym environment by providing an environment name. Here, we choose the [Cartpole environment](https://gymnasium.farama.org/environments/classic_control/cart_pole/). Feel free to take some time to read its documentation." + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Cart pole](./assets/fig14.gif)\n", + "> gif representing the cartpole environment taken from the official documentation" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see what information we can retrieve from the `env` variable..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'Action space: {env.action_space} ({env.action_space.n} possible actions)')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The action space is `Discrete(2)`, a discrete action space with 2 possible actions.\n", + "\n", + "A discrete action space means there is a finite set of actions that the agent can take, for example going left or right. On the contrary, a continuous action space means the actions can depend on various variables, like for example all the different ways you can move a pawn, as well as all the different ways you can move a knight and so on in a chess game." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'Observation space: {env.observation_space}')\n", + "print()\n", + "print(f'State shape: {env.observation_space.shape}')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Observation space is of shape (4,) meaning it has 4 values. We've printed the maximum and minimum for each of these values above.\n", + "\n", + "You can see that the first value's minimum is `-4.8` and it's maximum is `4.8`.\\\n", + "It corresponds to the cart's position.\n", + "\n", + "The second and fourth values ranges from `-infinity` to `infinity` (`3.8e+38` representing infinity in this case).\\\n", + "These values correspond to the Cart's velocity and the Pole's angular velocity respectively.\n", + "\n", + "The third value ranges from `-0.42` to `0.42`.\\\n", + "It represents the pole's angle in radians.\n", + "\n", + "[Read this part of the documentation for more details](https://gymnasium.farama.org/environments/classic_control/cart_pole/#observation-space)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright, know that we have these values, we have all we need to build our neural network because we know what our input size and action size are !" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's build our Neural Network which we will use as our policy function.\\\n", + "Its input will be the environment's state and its output will be a list of probabilities for each action.\\\n", + "You can do whatever you want with the hidden layer(s).\n", + "\n", + "As for the activations, apply ReLU for the first linear function followed by softmax for the output layer.\n", + "\n", + "> Use `env` to access the input and output sizes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define a neural network to model the policy\n", + "class NeuralNetwork(nn.Module):\n", + " def __init__(self, env):\n", + " super().__init__()\n", + "\n", + " # Create fully-connected layers with ReLU activations\n", + " self.fc1 = None\n", + " self.fc2 = None\n", + "\n", + " self.actions, self.states, self.rewards = [], [], []\n", + "\n", + " def forward(self, x):\n", + " # Convert the input tensor to a float tensor\n", + "\n", + " # Apply ReLU activations to the fully-connected layers\n", + "\n", + " # Apply a softmax activation to the final layer, to get probabilities for each action\n", + "\n", + " return x\n", + "\n", + "network = NeuralNetwork(env)\n", + "\n", + "# Use Adam optimizer to optimize the neural network\n", + "optim = None" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Neural Network](./assets/fig16.svg)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Awesome, now let's see what else gym can do !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "state, info = env.reset()\n", + "\n", + "print(state)\n", + "print()\n", + "print(info)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By using `env.reset()`, we have access to the four values inside our state ! If you reload this cell, you'll notice that these values are initialized randomly.\n", + "\n", + "We also receive an empty dictionary which for other environments can contain additional information.\\\n", + "From now on, we will be receiving `state, _` from `env.reset()` because we don't have any need for the `info` dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "action = env.action_space.sample() # use this method to get a random action from the action space\n", + "print(f\"We choose the action {action}...\")\n", + "\n", + "new_state, reward, termination, truncation, _ = env.step(action) # the last return is the info dictionary\n", + "\n", + "print(\"And we receive:\")\n", + "print()\n", + "print(f'Our new state: {new_state}') \n", + "print(f'The reward: {reward}')\n", + "print(f'Whether our episode was terminated: {termination}')\n", + "print(f'Or truncated: {truncation}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(env.render())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`env.render()` returns an rgb array representing our environment which we can plot using matplotlib's `imshow()` method !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from time import sleep\n", + "\n", + "\n", + "env = gym.make(env_name, render_mode=\"human\")\n", + "\n", + "for _ in range(5):\n", + " env.reset()\n", + " termination = False\n", + " while termination is not True:\n", + " _, _, termination, _, _ = env.step(env.action_space.sample())\n", + "\n", + "env.close()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this simple loop, we can see how our agent fares when it chooses an action at random. Not so well, huh ? Well let's train it using REINFORCE and see how it improves !\n", + "\n", + "Here's the REINFORCE algorithm as defined in Chapter 13 of [Sutton and Bartol's 'Reinforcement Learning: an Introduction'](http://incompleteideas.net/book/RLbook2020.pdf):\n", + "\n", + "![REINFORCE algorithm](./assets/fig15.svg)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's begin by setting up a few lists we'll be using for logging our reward and loss..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the deque class from the collections module\n", + "from collections import deque\n", + "\n", + "# Initialize empty lists for rewards and losses\n", + "recent_rewards = deque(maxlen=100)\n", + "train_rewards = []\n", + "train_loss = []\n", + "\n", + "# We will avoid rendering our environment during training: \n", + "# it would tremendously slow down the process\n", + "env = gym.make(env_name) " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, it's up to you to implement the REINFORCE algorithm using OpenAI Gym's Cartpole environment !\n", + "\n", + "Let's begin by defining our policy:\n", + "\n", + ">- Create a `policy_action()` method which returns an action based on the policy\n", + ">- Check out [Pytorch's Categorial Class](https://pytorch.org/docs/stable/distributions.html) which provides a great tool for probability distributions.\\\n", + ">The provided link explains its usage within REINFORCE in particular ! " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from torch.distributions import Categorical\n", + "\n", + "def policy_action(self, state):\n", + " # Get the probabilities for each action, using the current state\n", + "\n", + " # Create a distribution according to the probabilities\n", + "\n", + " # Sample an action from the distribution\n", + "\n", + " # Return the chosen action\n", + " pass\n", + "\n", + "NeuralNetwork.policy_action = policy_action" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next up, we need to define a simple method which stores our `action, state, reward` tuple at each time step.\n", + "\n", + ">- Simply add the arguments, `Action, State, Reward`, to their respective lists inside the `network` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def remember(self, Action, State, Reward):\n", + " pass\n", + " \n", + "NeuralNetwork.remember = remember" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to compute our discounted rewards for each time step, in other words, the 'G' variable in the algorithm:\n", + "\n", + "You can think of discounting as a way to help the agent become a better long-term planner as opposed to a short-term opportunist. We do this by discounting the value of rewards based on the time step.\n", + "\n", + "$$ G = \\sum_{k=t+1}^{T} \\gamma^{k-t-1} R_k $$" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What this means:\n", + "\n", + "- We need to return a list of `T` numbers (`T` being the episode length in steps)\n", + " - Each of these numbers ( $ \\sum^{T}_{k=t+1} $ ) is defined as such: \n", + " - $ \\gamma^{k-t-1} * R_k $\n", + "\n", + "- You are free to achieve this using either loops or `numpy` methods like [`power()`](https://numpy.org/doc/stable/reference/generated/numpy.power.html) and [`cumsum`](https://numpy.org/doc/stable/reference/generated/numpy.cumsum.html)\n", + "\n", + "- Don't forget we've declared a `gamma` constant at the top of the notebook !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def discount_rewards(self):\n", + " ## Discount the returns using the discount factor\n", + " pass\n", + "\n", + "NeuralNetwork.discount_rewards = discount_rewards\n", + "\n", + "network.rewards = [0.2, 0.6, 0.1, 1.2, 0.9]\n", + "network.discount_rewards()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Expected output: `[2.9602269 , 2.7602269 , 2.1632269 , 2.0642244 , 0.88213455]`\n", + "> If your values are close to these, it means you've correctly implemented the discounting." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's implement our gradient ascent !\n", + "\n", + "Since Adam, our optimizer, does most of the job for us, we only need to provide it the correct loss.\\\n", + "In REINFORCE, the loss is defined as follows:\n", + "\n", + "$$ L = G \\nabla \\ln \\pi(A_t|S_t,\\theta) $$\n", + "\n", + "The $ \\nabla $ symbol represents the gradient, so you can understand this formula as: `loss = G * log_prob` \n", + "\n", + "Because we are attempting to find the parameters of our policy which **maximize** the expected cumulative reward, we will not use the familiar **gradient descent** and instead use what is known as **gradient ascent**.\n", + "\n", + "Fear not, because it is not very complicated !\n", + "\n", + "With gradient descent, we update the parameters in the opposite direction of the gradient, which decreases the training cost or the expected cumulative reward, in this case.\n", + "\n", + "So in order to use gradient **ascent**, we must aim to **increase** the expected cumulative reward. Which means we \n", + "can achieve this by simply doing a `backward()` on a negative loss !\n", + "\n", + "This leads us back to the Monte Carlo formula for value-based methods:\n", + "\n", + "$$ G_t - V(S_t) $$\n", + "\n", + "or, for policy-based methods:\n", + "\n", + "$$ G[- \\nabla \\ln \\pi(A_t|S_t,\\theta)] $$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def gradient_ascent(self, discounted_rewards):\n", + " # Perform gradient ascent to update the probabilities in the distribution\n", + " for State, Action, G in zip(self.states, self.actions, discounted_rewards):\n", + " # Get the probabilities for the current state\n", + " probs = None\n", + "\n", + " # Calculate the loss as the negative log probability of the chosen action\n", + " # multiplied by the discounted return\n", + " loss = None\n", + "\n", + " # Clear the gradients, backpropagate the loss, and update the network parameters\n", + " \n", + " \n", + " \n", + " #\n", + "\n", + "NeuralNetwork.gradient_ascent = gradient_ascent" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All that remains is to use this `NeuralNetwork` class inside a training loop to make our agent learn how to solve Cartpole using the REINFORCE algorithm !\n", + "\n", + "We'll leave this part up to you ! (Use the screenshot of the REINFORCE pseudo code from earlier in the notebook for reference)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Iterate over the number of episodes\n", + "for episode in range(episodes):\n", + " # Reset the environment and initialize empty lists for actions, states, and rewards\n", + " state, _ = env.reset()\n", + " network.actions, network.states, network.rewards = [], [], []\n", + "\n", + " # Train the agent for a single episode\n", + " for _ in range(1000):\n", + " action = network.policy_action(state)\n", + "\n", + " # Take the action in the environment and get the new state, reward, and done flag\n", + " new_state, reward, termination, truncation, _ = env.step(action)\n", + "\n", + " # Save the action, state, and reward for later\n", + " network.remember(action, state, reward)\n", + "\n", + " state = new_state\n", + "\n", + " # If the episode is done or the time limit is reached, stop training\n", + " if termination or truncation:\n", + " break\n", + "\n", + " # Perform gradient ascent\n", + " network.gradient_ascent(network.discount_rewards())\n", + "\n", + " # Save the total reward for the episode and append it to the recent rewards queue\n", + " train_rewards.append(np.sum(network.rewards))\n", + " recent_rewards.append(train_rewards[-1])\n", + "\n", + " # Print the mean recent reward every 50 episodes\n", + " if episode % 50 == 0:\n", + " print(f\"Episode {episode:>6}: \\tR:{np.mean(recent_rewards):>6.3f}\")\n", + "\n", + " if np.mean(recent_rewards) > 400:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(train_rewards)\n", + "ax.plot(gaussian_filter1d(train_rewards, sigma=20), linewidth=4)\n", + "ax.set_title('Rewards')\n", + "\n", + "fig.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's display five episodes of our trained agent to see how glorious it is:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "env = gym.make(env_name, render_mode=\"human\")\n", + "\n", + "for _ in range(5):\n", + " Rewards = []\n", + " \n", + " state, _ = env.reset()\n", + " done = False\n", + " \n", + " for _ in range(1000):\n", + " # Calculate the probabilities of taking each action using the trained\n", + " # neural network\n", + " probs = network.forward(state)\n", + " \n", + " # Sample an action from the resulting distribution using the \n", + " # torch.distributions.Categorical() method\n", + " action = None\n", + " \n", + " new_state, reward, termination, truncation, _ = env.step(action)\n", + " \n", + " state = new_state\n", + "\n", + " Rewards.append(reward)\n", + "\n", + " if termination or truncation:\n", + " break\n", + " \n", + " # Print the total rewards for the current episode\n", + " print(f'Reward: {sum(Rewards)}')\n", + "\n", + "# Close the environment\n", + "env.close()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wow, you really did it ! You've succesfully implemented both a value-based and a policy-based method in reinforcement learning ! And, to top it all off, you even managed to solve CartPole using OpenAI Gym !\n", + "\n", + "If you're up for it, let's head over to section 2 and go **deeper** within the field of RL by returning to value-based methods and implementing the successor to Q-Learning, Deep Q Network, or DQN for short !\n", + "\n", + "Good luck !" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pool", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "6b483bbea0ef867292651300ca303e9b91f9a0c7db919f54df8d16a1790f2d11" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Day05/1.Introduction/assets/fig1.svg b/AI/Day05/1.Introduction/assets/fig1.svg new file mode 100644 index 0000000..149828e --- /dev/null +++ b/AI/Day05/1.Introduction/assets/fig1.svg @@ -0,0 +1,16 @@ + + + + + + + Machine LearningSupervised LearningUnsupervised LearningReinforcement Learning \ No newline at end of file diff --git a/AI/Day05/1.Introduction/assets/fig10.svg b/AI/Day05/1.Introduction/assets/fig10.svg new file mode 100644 index 0000000..cb5c9f1 --- /dev/null +++ b/AI/Day05/1.Introduction/assets/fig10.svg @@ -0,0 +1,16 @@ + + + + + + + 0.10.51.0-1.00.10.1-1.01.00.50.10.10.10.00.00.00.00.00.00.00.0State valueAction value \ No newline at end of file diff --git a/AI/Day05/1.Introduction/assets/fig11.svg b/AI/Day05/1.Introduction/assets/fig11.svg new file mode 100644 index 0000000..94551a2 --- /dev/null +++ b/AI/Day05/1.Introduction/assets/fig11.svg @@ -0,0 +1,16 @@ + + + + + + + ** \ No newline at end of file diff --git a/AI/Day05/1.Introduction/assets/fig12.svg b/AI/Day05/1.Introduction/assets/fig12.svg new file mode 100644 index 0000000..1216f9f --- /dev/null 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100644 index 0000000..93cec39 --- /dev/null +++ b/AI/Day05/1.Introduction/assets/fig8.svg @@ -0,0 +1,16 @@ + + + + + + + \ No newline at end of file diff --git a/AI/Day05/1.Introduction/assets/fig9.svg b/AI/Day05/1.Introduction/assets/fig9.svg new file mode 100644 index 0000000..a32ef9c --- /dev/null +++ b/AI/Day05/1.Introduction/assets/fig9.svg @@ -0,0 +1,16 @@ + + + + + + + TargetOld state valueOld state valueUpdated state value* learning rate** discount factor* \ No newline at end of file diff --git a/AI/Day05/1.Introduction/envi.py b/AI/Day05/1.Introduction/envi.py new file mode 100644 index 0000000..ef352a9 --- /dev/null +++ b/AI/Day05/1.Introduction/envi.py @@ -0,0 +1,90 @@ +""" +In this file we define the environment class to avoid filling the notebook with too much code +""" +import random +import numpy as np + +GRAPHICS = {' ': [255, 255, 255], + 'O': [255, 0, 0], + 'P': [0, 0, 255], + 'X': [0, 255, 0]} + +REWARDS = {'NEGATIVE': -100, + 'NEUTRAL': -1, + 'POSITIVE': 1000} + + +class Environment: + """ + The environment which will receive actions and update state + """ + + def __init__(self, map_size, actions): + self.map_size = map_size + self.actions = actions + + # generating a random map + self.map = [' '] * self.map_size + for i in range(self.map_size): + self.map[i] = [' '] * self.map_size + obstacle = random.randint(0, self.map_size - 1) + for j in range(self.map_size): + if i * self.map_size + j == self.map_size ** 2 - 1: + self.map[i][j] = 'X' + continue + if i != 0 and i != self.map_size - 1 and j == obstacle: + self.map[i][j] = 'O' + obstacle = True + # spawning obstacles + self.dangers = [] + for y in range(self.map_size): + for x in range(self.map_size): + if self.map[y][x] == 'O': + self.dangers.append(y*self.map_size+x) + # initialising agent state + self.state = 0 + + def graphic(self): + """ + This method will return an array of colors which can be used for animation in matplotlib + """ + color_array = [[GRAPHICS[x] for x in y] for y in self.map] + color_array[self.state // self.map_size][self.state % + self.map_size] = GRAPHICS['P'] + return np.array(color_array).repeat(10, axis=0).repeat(10, axis=1) + + def step(self, action): + """ + This method will update the environment based on the chosen action by the agent + """ + new_state = self.state + reward = 0 + done = False + + # movement + if action == self.actions['UP']: + new_state -= self.map_size + if action == self.actions['DOWN']: + new_state += self.map_size + if action == self.actions['LEFT'] and new_state % self.map_size != 0: + new_state -= 1 + if action == self.actions['RIGHT'] and new_state % self.map_size != self.map_size - 1: + new_state += 1 + if 0 <= new_state < self.map_size ** 2: + self.state = new_state + # granting rewards + reward = REWARDS['NEUTRAL'] + if self.state in self.dangers: + reward = REWARDS['NEGATIVE'] + done = True + if self.state == self.map_size ** 2 - 1: + reward = REWARDS['POSITIVE'] + done = True + return self.state, reward, done + + def reset(self): + """ + This method resets our environment to default values + """ + self.state = 0 + return self.state diff --git a/AI/Day05/2.GoingDeeper/README.md b/AI/Day05/2.GoingDeeper/README.md new file mode 100644 index 0000000..3c011e6 --- /dev/null +++ b/AI/Day05/2.GoingDeeper/README.md @@ -0,0 +1,118 @@ +# Going Deeper with Deep Q Networks + +![Lunar Lander](assets/fig1.gif) +> An agent solving the [Lunar Lander](https://gymnasium.farama.org/environments/box2d/lunar_lander/) environment + +Now that you've seen examples of both value and policy based methods for RL, let's take a deeper dive into the first one by implementing the Deep Q Network algorithm, which is what you get when you apply Deep Neural Networks to Q-Learning ! + +## 1. Why do we need neural networks ? + +We were able to train pretty good agents and receive nice rewards using simple Q-Learning by creating a Q-Table and updating its values. + +![Environment comparison](assets/fig2.svg) + +CartPole is one of the simpler Gym environments, with only four values in its state space and only two possible actions to be taken.\ +With more and more complex environments, we need to use neural networks to approximate our Q-Table ! + +![Q-Table](assets/fig3.svg) + +We have learned, during this week, of a function which allows to take an input and output a prediction based on that input. + +![DQN](assets/fig4.svg) + +The Deep Q Network can replace our Q-Table. It is a deep neural network which takes a state as input and outputs the q-values of each action within the state ! + +## Deep Q Network + +The goal of this exercise is to implement the following algorithm using PyTorch and OpenAI Gym to solve the Lunar Lander environment ! + +![Algorithm](assets/fig5.svg) +> The Deep Q Network as defined in [Playing Atari with Deep Reinforcement Learning](https://arxiv.org/pdf/1312.5602v1.pdf) by Mnih et al. + +### Dissecting the algorithm: + +The first thing you can do with this algorithm is to extract the different variables or constants: + +_Constants_: +- **Memory capacity** $N$ +- **Training length** $M$, the amount of episodes before the training ends +- **Episode length** $T$, the amount of time steps before the episode ends + - and its **timestep** $t$ +- **Discount rate $\gamma$**, as you know, usually $\gamma = 0.99$ + +_Variables_: +- **Transitions $(\phi_t, a_t, r_t, \phi_{t+1})$**: a tuple containing the current state $\phi$, the action $a$, the reward $r$ and the new state $\phi$ for each time step $t$ +- **Memory $D$**: a list of size $N$ containing $Transitions$ +- **Action-value function $Q$**: a neural network +- **sequence $s$ and preprocessed sequence $\phi$**: using gym, we can simply consider that our sequence is already preprocessed and that $\phi$ is the state we receive when we call `env.reset()` or `env.step()`. +- **Parameters $\theta$** these are the parameters of our neural network $Q$ + +_Methods_: +- **Execute action in emulator and observe reward and image**: the usual OpenAI Gym implementation: + ```py + state, reward, termination, truncation, _ = env.step(action) + ``` +- **For terminal / non-terminal state**: this is where `termination` finally comes in handy inside our algorithms ! + > Tip: you can implement this condition in one line by replacing the formula with: + > $$ y_j = r_j + (termination_j - 1) * \gamma * max_{a'} * Q(\phi_{j+1}, a'; \theta) $$ + > Because `termination` is a boolean, it means the result will be reduced to + > $$ y_j = r_j $$ + > when `termination` is equal to `True` +- **Gradient descent step**: the usual PyTorch implementation, nothing new here: + ```py + optimizer.zero_grad() ## reset the gradients + loss.backward() ## backward propagation + optimizer.step() ## updating the network + ``` + + +### Lunar Lander + +You need to solve the Lunar Lander environment, so read the [documentation](https://gymnasium.farama.org/environments/box2d/lunar_lander/) carefully. + +```py +env = gym.make("LunarLander-v2") +``` +> Bonus: your implementation should support the setting of parameters such as learning rate, discount rate, memory capacity, episode length, etc. from the command line, as such: +> ```py +> python3 dqn.py --lr 5e-4 --gamma 0.99 -M 500 -N 1000 -T 1000 +> ``` + +**Good luck !** + +## DQN Extensions + +In order to improve upon the base DQN algorithm, many different extensions were made. + +We would like to ask you to implement this one, defined in [Human-level control through deep reinforcement learning](https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf) by Mnih et al. + +It introduces the concept of a **target network**, which allows for the network to compare its predictions with a stable network while updating its "**online network**". + +This prevents a problem with the vanilla DQN, where the network would attempt to minimize its loss by getting closer to its own prediction, meaning it would be like a pig chasing a carrot that is attached to a rod carried by the human that's riding it. + +![Illustration of the problem with vanilla DQN](./assets/fig7.svg) + +With this extension, we use two separate neural networks: +- The **online** network, which is used for prediction. + - This network's parameters are: + - initialized randomly. + - updated at each timestep according to the loss. +- The **target** network, which is used to compute the loss. + - This network's parameters are: + - initialized to the same values as those of the **online** network. + - updated every **C** timesteps, but instead of updating them with an optimizer, we replace its parameters with those of the **online** network. + +Here's the updated algorithm: + +![DQN with target network](./assets/fig6.svg) + +There aren't many changes, as you can see.\ +Let's go through each difference: + +- First, we initialize a second neural network using the same parameters as the first. +- Then, instead of using the same network for our predictions and to compute our target, we use the second network instead. +- Finally, we reset its parameters to those of the first network every **C** steps. + +See if you can implement this extension into your network and observe the differences in performance ! + +**Good luck !** \ No newline at end of file diff --git a/AI/Day05/2.GoingDeeper/assets/fig1.gif b/AI/Day05/2.GoingDeeper/assets/fig1.gif new file mode 100644 index 0000000..3c6a165 Binary files /dev/null and b/AI/Day05/2.GoingDeeper/assets/fig1.gif differ diff --git a/AI/Day05/2.GoingDeeper/assets/fig2.svg b/AI/Day05/2.GoingDeeper/assets/fig2.svg new file mode 100644 index 0000000..7383f5c --- /dev/null +++ b/AI/Day05/2.GoingDeeper/assets/fig2.svg @@ -0,0 +1,16 @@ + + + + + + + CartPole State SpaceLunarLander State Space \ No newline at end of file diff --git a/AI/Day05/2.GoingDeeper/assets/fig3.svg b/AI/Day05/2.GoingDeeper/assets/fig3.svg new file mode 100644 index 0000000..eb56ffd --- /dev/null +++ b/AI/Day05/2.GoingDeeper/assets/fig3.svg @@ -0,0 +1,16 @@ + + + + + + + StateQ-TableAction \ No newline at end of file diff --git a/AI/Day05/2.GoingDeeper/assets/fig4.svg b/AI/Day05/2.GoingDeeper/assets/fig4.svg new file mode 100644 index 0000000..f9fa9c6 --- /dev/null +++ b/AI/Day05/2.GoingDeeper/assets/fig4.svg @@ -0,0 +1,16 @@ + + + + + + + StateDQNAction \ No newline at end of file diff --git a/AI/Day05/2.GoingDeeper/assets/fig5.svg b/AI/Day05/2.GoingDeeper/assets/fig5.svg new file mode 100644 index 0000000..8e62a18 --- /dev/null +++ b/AI/Day05/2.GoingDeeper/assets/fig5.svg @@ -0,0 +1,16 @@ + + + + + + + \ No newline at end of file diff --git a/AI/Day05/2.GoingDeeper/assets/fig6.svg b/AI/Day05/2.GoingDeeper/assets/fig6.svg new file mode 100644 index 0000000..19dabf6 --- /dev/null +++ b/AI/Day05/2.GoingDeeper/assets/fig6.svg @@ -0,0 +1,16 @@ + + + + + + + \ No newline at end of file diff --git a/AI/Day05/2.GoingDeeper/assets/fig7.svg b/AI/Day05/2.GoingDeeper/assets/fig7.svg new file mode 100644 index 0000000..d86f295 --- /dev/null +++ b/AI/Day05/2.GoingDeeper/assets/fig7.svg @@ -0,0 +1,16 @@ + + + + + + + \ No newline at end of file diff --git a/AI/Day05/README.md b/AI/Day05/README.md new file mode 100644 index 0000000..d575e43 --- /dev/null +++ b/AI/Day05/README.md @@ -0,0 +1,19 @@ +# ~ PoC AI Pool 2025 ~ + +- ## Day 5: Reinforcement Learning + - ### Module 1: Q Learning + - **Notebook:** [`Q_Learning.ipynb`](./1.Introduction/Q_Learning.ipynb) + - ### Module 2: REINFORCE + - **Notebook:** [`REINFORCE.ipynb`](./1.Introduction/REINFORCE.ipynb) + - ### Module 3: Deep Q Network + - **Module:** [`2.GoingDeeper`](./2.GoingDeeper) + +--- + +Today, the focus is Reinforcement Learning : where machines can learn to adapt to environments and take actions based on their observations ! + +> Here's a list of resources that we believe can be useful to follow along (and that we've ourselves used to learn these topics before being able to write the subjects): + +- [Reinforcement Learning: An Introduction](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf) +- [Huggingface Deep RL Course](https://huggingface.co/deep-rl-course/unit0/introduction) +- [Gymnasium](https://gymnasium.farama.org/index.html) diff --git a/AI/Day05/requirements.txt b/AI/Day05/requirements.txt new file mode 100644 index 0000000..30cf277 --- /dev/null +++ b/AI/Day05/requirements.txt @@ -0,0 +1,5 @@ +seaborn +scipy +torch +gym +tensorboard diff --git a/AI/README.md b/AI/README.md new file mode 100644 index 0000000..2975efc --- /dev/null +++ b/AI/README.md @@ -0,0 +1,76 @@ +# ~ PoC AI Pool 2026 ~ + +Welcome to the **PoC AI Pool 2026** ! During this week, you will discover the world of Artificial Intelligence, from Python basics to reinforcement learning, through machine learning, deep learning and large language models. + +## Program + +| Day | Topic | Description | +|-----|-------|-------------| +| [Day 01](Day01) | **Python Basics** | Python fundamentals, NumPy, Matplotlib and Pandas | +| [Day 02](Day02) | **Large Language Models** | Fine-tuning a pre-trained model and building a RAG system | +| [Day 03](Day03) | **Machine Learning** | Linear regression, logistic regression and neural network theory | +| [Day 04](Day04) | **Neural Networks** | PyTorch, vision models (MNIST, CIFAR) and building your own framework | +| [Day 05](Day05) | **Reinforcement Learning** | Q-Learning, REINFORCE and Deep Q-Networks | + +## Getting Started + +Before starting the pool, please follow the [setup guide](setup.md) to configure your environment (virtual environment or Docker). + +### Prerequisites + +- Python 3.x and `pip` +- Jupyter Notebook +- Git + +### Quick Start + +```bash +git clone +cd AI +python3 -m venv venv +source venv/bin/activate +pip install -r requirements.txt +``` + +Then navigate to the day's folder and open the notebooks. + +## Structure + +``` +AI/ +├── Day01/ # Python, NumPy, Matplotlib, Pandas +├── Day02/ # Fine-tuning, RAG +├── Day03/ # Linear & Logistic Regression, Neural Networks +├── Day04/ # PyTorch, Vision Models, MyTorch +├── Day05/ # Reinforcement Learning +├── setup.md # Environment setup guide +└── requirements.txt +``` + +Each day has its own README with detailed instructions and resources. + +

+Organization +

+
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+ + LinkedIn logo + + + Instagram logo + + + Twitter logo + + + Discord logo + +

+

+ + Website logo + +

+ +> 🚀 Don't hesitate to follow us on our different networks, and put a star 🌟 on `PoC's` repositories. diff --git a/AI/image.png b/AI/image.png new file mode 100644 index 0000000..0dbaa58 Binary files /dev/null and b/AI/image.png differ diff --git a/AI/requirements.txt b/AI/requirements.txt new file mode 100644 index 0000000..0d36e69 --- /dev/null +++ b/AI/requirements.txt @@ -0,0 +1,26 @@ +jupyter==1.1.1 +notebook==7.3.2 +matplotlib==3.10.0 +numpy==1.26.3 +pandas==2.2.3 +seaborn==0.13.2 +scikit-learn==1.6.1 +requests==2.32.3 +gym==0.26.2 +box2d-py==2.3.8 +gym[classic_control]==0.26.2 +einops==0.8.0 +torchvision==0.12.0 +pillow==10.2.0 +scipy==1.15.1 +IPython==8.20.0 +moviepy==2.1.2 +envi==0.2.2 +tensorboard==2.18.0 +datasets==3.2.0 +nltk==3.9.1 +torchvision==0.12.0 +gradio==3.0.2 +prophet==1.1.2 +h2o==3.38.0.4 +tqdm==4.67.1 \ No newline at end of file diff --git a/AI/setup.md b/AI/setup.md new file mode 100644 index 0000000..9a71a1c --- /dev/null +++ b/AI/setup.md @@ -0,0 +1,75 @@ +# Python Setup Guide + +This setup guide provides instructions to quickly get started with the AI Pool using a virtual environment (`venv`), `pip` and a `requirements.txt` file, or, as an alternative option, Docker. + +## Requirements +- Python 3.x and `pip` installed on your machine (for venv setup). +- Docker installed on your machine (for Docker setup). + +## Option 1: Local Setup with Virtual Environment + +If you want to set up the environment locally on your machine, you can use a Python virtual environment (`venv`). + +### 1. Create and Activate the Virtual Environment + +Navigate to your project directory and create a new virtual environment: + +```bash +python3 -m venv poc_ai_pool_venv +``` + +Activate the virtual environment: + +- On **macOS/Linux**: + + ```bash + source poc_ai_pool_venv/bin/activate + ``` + +- On **Windows**: + + ```bash + poc_ai_pool_venv\Scripts\activate + ``` + +### 2. Install Required Packages + +Ensure that you have a `requirements.txt` file in your project directory, containing all the necessary dependencies for the pool. + +To install the required packages listed in the `requirements.txt`, run: + +```bash +pip install -r requirements.txt +``` + +## Classic error with vscode + +- **Virtual Environment is activated in your terminal, but not on your vscode** + - Ensure that the virtual environment is properly activated by checking the bottom right of your status bar. + + +## Option 2: Docker Setup + +### 1. Pull the Docker Image + +To begin, pull the pre-built Docker image that contains the necessary setup for the pool. Run the following command in your terminal: + +```bash +docker pull laiheau/poc_ai_pool +``` + +### 2. Run the Docker Container + +Once the image is pulled, you can start a Docker container that will run the environment. Execute this command: + +```bash +docker run -t -i -v .:/workspace:z -p 8888:8888 laiheau/poc_ai_pool +``` + +This will start a jupyter notebook server, **and copy your current directory in it**. You read this right, your current directory will be synchronized each seconds with the one in your jupyter notebook server. Then, you just need to follow the link in your terminal (see the picture bellow). + +![connection to server example picture](image.png) + +## Conclusion + +You now have two options for setting up the Poc AI Pool environment: using a local Python virtual environment or Docker. Both methods will give you access to Jupyter Notebooks and all the tools needed for the pool. Enjoy this week with us !