AlphaZero from Scratch – Machine Learning Tutorial

Dive into a comprehensive machine learning tutorial that guides you through building AlphaZero from scratch. Learn how to create a game-playing algorithm using artificial intelligence and machine learning techniques to achieve superhuman performance in board games. Explore key concepts such as Monte Carlo Tree Search (MCTS) and AlphaMCTS through detailed explanations and practical implementations. Progress through ten chapters covering various aspects, including TicTacToe, model development, self-play, training, and evaluation. Apply your knowledge to different games like ConnectFour and discover advanced techniques such as parallel processing. Access provided code and trained models for each chapter, and refer to the original AlphaZero paper for in-depth understanding. Gain hands-on experience in developing cutting-edge AI algorithms over the course of 4-5 hours, created by Robert Förster.

⌨️ Introduction
⌨️ Overview – Part 1
⌨️ MCTS-Explained
⌨️ AlphaMCTS-Explained
⌨️ Overview – Part 2
⌨️ Chapter 1: TicTacToe
⌨️ Chapter 2: MCTS
⌨️ Chapter 3: Model
⌨️ Chapter 4: AlphaMCTS
⌨️ Chapter 5: AlphaSelfPlay
⌨️ Chapter 6: AlphaTrain
⌨️ Chapter 7: AlphaTweaks
⌨️ Chapter 8: ConnectFour
⌨️ Chapter 9: AlphaParallel
⌨️ Chapter 10: Eval