Awesome Reinforcement Learning - A handpicked collection of Lectures, Books, Surveys, Papers / Thesis, Codes, Tutorials / Websites, Online Demos and Open Source Reinforcement Learning Platforms related to Reinforcement Learning. Machine Learning Cheat Sheets - This repository aims at summing up in the same place all the important notions that are covered in Stanford's CS 229 Machine Learning course, and include: Refreshers in related topics that highlight the key points of the prerequisites of the course and Cheatsheets for each machine learning field, as well as another dedicated to tips and tricks to have in mind when training a model. Deep Learning Papers - This repo covers the most cited papers on various topics like Image Segmentation / Object Detection, Natural Language Processing / RNNs, Reinforcement Learning / Robotics, Convolutional Network Models, Unsupervised / Generative Models, and more. Machine learning Basics - This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6). All algorithms are implemented from scratch without using additional machine learning libraries.