Neural Networks For Chess

Download or read book Neural Networks For Chess written by Dominik Klein and published by Independently Published. This book was released on 2021-09-28 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Neural Networks have revolutionized computer engines for Go, Shogi and chess. Finally computers are able to evaluate a game position similiar to the way human experts do it. By that, computers are able to identify long-term strategic advantages and disadvantages. But how do chess engines based on neural networks such as AlphaZero, Leela Chess Zero actually work? This book gives an answer to that question. With lots of practical examples and illustrations, all basic building blocks that are required to understand modern chess are introduced. Based on that, the concepts of both classic and modern chess engines are explained. Finally, a miniature version of AlphaZero to play the game Hexapawn is implemented in Python. Chapters include: Single-Layer and Multilayer Perceptrons, Back-Propagation and Gradient Descent, Classification and Regression, Network Vectorization, Convolutional Layers, Squeeze and Excitation Networks, Fully Connected Layers, Batch Normalization, Rectified Linear Unit (ReLU), Residual Layers, Minimax, Alpha-Beta Search, Monte-Carlo Tree Search, AlphaGo, AlphaGo Zero, AlphaZero, Leela Chess Zero (Lc0), Fat Fritz, Effectively Updateable Neural Networks, Fat Fritz 2, Maia, Supervised Learning Hexapawn, Reinforcement Learning of Hexapawn (Hexapawn Zero)