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:
Bash:
Git:
Cloud:
GitBook:
Complexities:
VS-Code:
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
- π’ 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 β¨
Libraries:
- jQuery β 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
- π 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:
Golang:
Must have Chrome-Extensions:
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:
iOS:
Mac:
CONDA:
Game Engine
Blockchain:
Interview Questions:
Tech Podcasts:
Newsletter
GitHub Readme Generator:
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.