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Introduction To Tensorflow

I have made 3 notebooks on Introduction to Tensorflow:

1. Tensorflow_Hello_World.ipynb

I have picked a parabolic log function and tried to train a shallow deep network on a few samples to generate the same function. The goal of this notebook is to give you an idea that how to build a simple network in Tensorflow.

2. Tensorflow_Intro_NLP.ipynb

I have picked a dataset from kaggle related to fake-news and tired to show the steps you need to go through for solving an NLP based problem

3. Tensorflow_Computer_Vision.ipynb

I have taken the motivation to build this notebook from "TensorFlow in Practice Specialization - Coursera". It has covered important concepts of CV like convolution and pooling. It has also shown how to visualize images in the layers.

Presentation

You can find link to the presentation related to this at: https://www.slideshare.net/rayyankhalid35/introduction-to-tensorflow-213058272

Instructions for setup

  1. Download and install python from python.org Version: minimum python version required 3.5 URL: https://www.python.org/downloads/

  2. Get the notebooks codebase

    git clone https://github.com/rayyan17/Introduction-To-Tensor-Flow.git <path-to-project-dir>

  3. Create and activate virtual environment.

    cd <path-to-project-dir>
python3 -m venv .python3_venv       # creates the virtual env
source .python3_venv/bin/activate   # activates the virtual env

    After activating the virtual env. Any installed python packages would be installed here without affecting the main python binaries. To de-activate the virtual env, just type "deactivate"

  4. Install Required Packages

    pip install -r requirements.txt

Run your Notebook

Run you Notebook using the following command:

```bash
jupyter notebook NOTEBOOK_NAME
```