Algorithms, Cheat-Sheet, and Resources

Collection of Useful Algorithms in multiple programming languages and some awesome cheat-sheet and resources for learning geeky stuffs.

Please find below the links to awesome cheat-sheet and resources:

Multiple platforms:

• A ridiculous collection of cheatsheets
• Awesome cheatsheets for popular programming languages, frameworks and development tools
• 📓 UI component dev & test: React, Vue, Angular, React Native, Ember, Web Components & more!
• Awesome lists about all kinds of interesting topics
• List of Development YouTube Channels
• 📝 A curated list of Web Performance Optimization
• 💻📖 Laws, Theories, Principles and Patterns that developers will find useful. #hackerlaws
• A Collection of application ideas which can be used to improve your coding skills
• Teach Yourself Computer Science
• 🤓 Build your own (insert technology here)
• Google's Engineering Practices documentation
• A list of Free Software network services and web applications which can be hosted locally
• Real-time performance monitoring, done right!
• 🎓 Path to a free self-taught education in Computer Science!
• 📚 List of awesome university courses for learning Computer Science!
• Deploy a Production Ready Kubernetes Cluster

Bash:

• 📖 A collection of pure bash alternatives to external processes
• Utility bash scripts to do automatable tasks with a single command

Git:

• Git Commands
• Simple terminal UI for git commands
• A gitmoji interactive command line tool for using emojis on commits 💻

Cloud:

• 📙 Amazon Web Services — a practical guide
• The Google Cloud Developer's Cheat Sheet

GitBook:

• GitBook - A command-line tool(and Node.js library) for building beautiful books using GitHub/Git and Markdown

Complexities:

• Big O Cheatsheet

VS-Code:

• Keyboard Shortcuts for Windows
• Keyboard Shortcuts for macOS

Javascript:

Resources:

• Must-watch videos about javascript
• A curated list of javascript fundamentals and algorithms
• This is about useful JS tips!
• A book about JavaScript, programming, and the wonders of the digital
• 🐢 A collection of awesome browser-side JavaScript libraries, resources and shiny things
• LazyLoad is a fast, lightweight and flexible script that speeds up your web application by loading your content images, videos and iframes only as they enter the viewport
• A book series on JavaScript. @YDKJS on twitter
• A long list of (advanced) JavaScript questions, and their explanations ✨

Libraries:

• jQuery — New Wave JavaScript
• React.js
• lodash - A modern JavaScript utility library delivering modularity, performance, & extras
• Underscore.js - A utility-belt library for JavaScript
• Knockout - A JavaScript MVVM (a modern variant of MVC) library
• Polymer - library which enables cross-browser support for HTML5 web components
• Textures.js is a JavaScript library for creating SVG patterns
• Lightweight fuzzy-search, in JavaScript
• A practical functional library for JavaScript programmers
• A table library that works everywhere

Frameworks:

• Angular.js
• Vue.js
• Ext JS
• Backbone.js
• Next.js
• Ember.js - A JavaScript framework for creating ambitious web applications
• Fastify - Fast and low overhead web framework, for Node.js
• Alpine - A rugged, minimal framework for composing JavaScript behavior in your markup
• 📊 Cube.js - Open Source Analytics Framework

General-Purpose Task Runners:

• Webpack
• Gulp.js
• Grunt

Module Bundlers:

• Browserify
• RequireJS

Linting:

• ESLint
• JSHint
• JSLint

Test Suits:

• Jest
• Mocha
• Jasmine

Python:

• Cool Python features for machine learning
• A collection of Python Scripts made for fun, while exploring Python 🐍
• Tutorials on various Python modules and its applications
• Python Tutorials in Detail

  Packages:

  • Pandas
  • Numpy
  • Matplotlib
  • scikit-learn

Golang:

• Standard Go Project Layout

Must have Chrome-Extensions:

• Momentum
• Adblock Plus
• uBlock Origin
• Wappalyzer
• GitHub Repository Size
• Grammarly
• Isometric Contributions
• WhatFont
• React Developer Tools
• Octotree
• Refined GitHub
• JSON Formatter
• Angular DevTools
• Vue.js devtools
• GoFullPage - Full Page Screen Capture
• Fake Filler

Machine-Learning/Data Science/AI/DL:

• PyTorch tutorials
• Data-Science Cheat-Sheet
• Related papers for robust machine learning
• Pretrained EfficientNet, MixNet, MobileNetV3, MNASNet A1 and B1, FBNet, Single-Path NAS
• A booklet on machine learning systems design with exercises
• A collection of datasets ready to use with TensorFlow
• code for Data Science From Scratch book
• Tutorials, assignments, and competitions for MIT Deep Learning related courses
• Fast image augmentation library and easy to use wrapper around other libraries
• Collection of Artificial Intelligence Algorithms implemented on various problems
• PyTorch image models, scripts, pretrained weights
• Code for visualizing the loss landscape of neural nets
• Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
• Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
• An end-to-end PyTorch framework for image and video classification
• The simplest way for researchers and developers to build world-class ML solutions
• Pose Animator takes a 2D vector illustration and animates its containing curves in real-time
• A system for detecting human body, facial and foot keypoints from RGB images
• A YouTube channel that covers recent and interest research works in the ML and DL area
• Tensorflow 2.0 cheat sheet
• A list of ML papers, along with their respective code links

React/React-Native:

• React-Typescript Cheatsheet
• Lint rules related to React & JSX for TSLint
• 🐐 Simple and complete React DOM testing utilities that encourage good testing practices
• React Native Awesome Button
• React Native Awesome Slider
• Simple and complete React DOM testing utilities
• Jest utilities for Styled Components
• Collection of useful React components
• Build forms in React, without the tears 😭
• High performance <canvas> rendering for React components
• 🌊 A flexible and fun JavaScript file upload library
• A React-Native datetime-picker for Android and iOS
• Galio is a beautifully designed, Free and Open Source React Native Framework
• A description of the conceptual model of React without implementation burden
• Recoil - An experimental state management library for React apps
• The recommended Code Splitting library for React ✂️✨
• A draggable and resizable grid layout with responsive breakpoints, for React
• A collection of beautiful and useful React hooks to speed-up your components and hooks development

iOS:

•  A curated list of Open Source example iOS apps developed in Swift

Mac:

•  Awesome Mac premium software in various categories
• 🕹 Retro video game emulation for macOS

CONDA:

• CONDA CHEAT SHEET

Game Engine

• Babylon.js is a powerful, beautiful, simple, and open game and rendering engine packed into a friendly JavaScript framework
• Fast and lightweight WebGL game engine

Blockchain:

• A full stack for bitcoin and blockchain-based applications

Interview Questions:

• Tech interview prep list
• A curated awesome list of lists of interview questions. Feel free to contribute! 🎓
• 101+ coding interview problems
• 🕸 Almost complete answers to "Front-end Job Interview Questions" which you can use to interview potential candidates, test yourself or completely ignore
• JavaScript interview Questions
• Data Structures and Algorithms in JavaScript
• Pramp - Practice live interviews with peers
• Coding Interview University
• Interview Company Guides

Tech Podcasts:

• Curated List of Mostly Active Dev Podcasts

Newsletter

• Nibbles
• Bytes

GitHub Readme Generator:

• 🚀 This tool contains mini GUI components that you can hook together to automatically generate markdown code for a perfect readme

Want to Contribute ?

Can't see your favorite algorithm script or cheat-sheet? Send a PR for adding your favorite algorithm in any programming language.

License

This repository is available under the MIT License.

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P.S.: Please make sure the file name itself is self-explanatory about the Algorithm or make a folder along containing your script and a README file explaining the necessary details. Also, make sure there is main(): function which is taking clear inputs(if any) which could be easily understood by any person who wants to use that algorithm.