Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Related tags

Deep Learningchessdeep-learningalphazeronnue
Overview

Neural Networks For Chess

cover

Free Book

  • Grab your free PDF copy HERE
  • Buy a printed copy at HERE or HERE

Donations are welcome:

paypal

Contents

AlphaZero, Leela Chess Zero and Stockfish NNUE revolutionized Computer Chess. This book gives a complete introduction into the technical inner workings of such engines.

The book is split into four chapters:

  1. The first chapter introduces neural networks and covers all the basic building blocks that are used to build deep networks such as those used by AlphaZero. Contents include the perceptron, back-propagation and gradient descent, classification, regression, multilayer perpectron, vectorization techniques, convolutional netowrks, squeeze and exciation networks, fully connected networks, batch normalization and rectified linear units, residual layers, overfitting and underfitting.

  2. The second chapter introduces classical search techniques used for chess engines as well as those used by AlphaZero. Contents include minimax, alpha-beta search, and Monte Carlo tree search.

  3. The third chapter shows how modern chess engines are designed. Aside from the ground-breaking AlphaGo, AlphaGo Zero and AlphaZero we cover Leela Chess Zero, Fat Fritz, Fat Fritz 2 and Effectively Updateable Neural Networks (NNUE) as well as Maia.

  4. The fourth chapter is about implementing a miniaturized AlphaZero. Hexapawn, a minimalistic version of chess, is used as an example for that. Hexapawn is solved by minimax search and training positions for supervised learning are generated. Then as a comparison, an AlphaZero-like training loop is implemented where training is done via self-play combined with reinforcement learning. Finally, AlphaZero-like training and supervised training are compared.

Source Code

Just clone this repository or directly browse the files. You will find here all sources of the examples of the book.

About

During COVID, I worked a lot from home and saved approximately 1.5 hours of commuting time each day. I decided to use that time to do something useful (?) and wrote a book about computer chess. In the end I decided to release the book for free.

Profits

To be completely transparent, here is what I make from every paper copy sold on Amazon. The book retails for $16.95 (about 15 Euro).

  • printing costs $4.04
  • Amazon takes $6.78
  • my royalties are $6.13

Errata

If you find mistakes, please report them here - your help is appreciated!

You might also like...
Hand gesture recognition based whiteboard that allows you to write on live webcam. This is the first version and has features like 4 different colors, eraser and a recording option that records your session and saves it in a "recordings" folder. Use index finger to draw and two or more fingers to move around and select items. Future version will contain more functionalities like changeable thickness, color palette, integration with zoom and google meet etc.

hand-write Hand gesture recognition based whiteboard that allows you to write on live webcam. This is the first version and has features like 4 differ

null 27 Dec 16, 2022
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Daniel Bourke 3.4k Jan 7, 2023
PyTorch implementation of 1712.06087
PyTorch implementation of 1712.06087 "Zero-Shot" Super-Resolution using Deep Internal Learning

Unofficial PyTorch implementation of "Zero-Shot" Super-Resolution using Deep Internal Learning Unofficial Implementation of 1712.06087 "Zero-Shot" Sup

Jacob Gildenblat 196 Nov 27, 2022
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

DataFree A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation" Authors: Gongfa

ZJU-VIPA 47 Jan 9, 2023
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

FuseDream This repo contains code for our paper (paper link): FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizat

XCL 191 Dec 31, 2022
Free-duolingo-plus - Duolingo account creator that uses your invite code to get you free duolingo plus
Free-duolingo-plus - Duolingo account creator that uses your invite code to get you free duolingo plus

free-duolingo-plus duolingo account creator that uses your invite code to get yo

null 1 Jan 6, 2022
Free like Freedom

This is all very much a work in progress! More to come! ( We're working on it though! Stay tuned!) Installation Open an Anaconda Prompt (in Windows, o

null 2.3k Jan 4, 2023
MLSpace: Hassle-free machine learning & deep learning development

MLSpace: Hassle-free machine learning & deep learning development

abhishek thakur 293 Jan 3, 2023
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

Advances in Financial Machine Learning Exercises Experimental solutions to selected exercises from the book Advances in Financial Machine Learning by

Brian 1.4k Jan 4, 2023
Comments
  • 'Board' object has no attribute 'outcome'

    'Board' object has no attribute 'outcome'

    I just executed python mcts.py and received an error message: 34 0 Traceback (most recent call last): File "mcts.py", line 134, in payout = simulate(node) File "mcts.py", line 63, in simulate while(board.outcome(claim_draw = True) == None): AttributeError: 'Board' object has no attribute 'outcome'

    opened by barvinog 5
  • Invalid Reduction Key auto.

    Invalid Reduction Key auto.

    Thank you for the source code of Chapter 5. I executed python mnx_generateTrainingData.py - OK Then python sup_network.py - OK

    Then I executed python sup_eval.py and got the error :

    Traceback (most recent call last): File "sup_eval.py", line 6, in model = keras.models.load_model("supervised_model.keras") File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/saving.py", line 492, in load_wrapper return load_function(*args, **kwargs) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/saving.py", line 584, in load_model model = _deserialize_model(h5dict, custom_objects, compile) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/saving.py", line 369, in _deserialize_model sample_weight_mode=sample_weight_mode) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py", line 75, in symbolic_fn_wrapper return func(*args, **kwargs) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 229, in compile self.total_loss = self._prepare_total_loss(masks) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 692, in _prepare_total_loss y_true, y_pred, sample_weight=sample_weight) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/losses.py", line 73, in call losses, sample_weight, reduction=self.reduction) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/utils/losses_utils.py", line 156, in compute_weighted_loss Reduction.validate(reduction) File "/home/barvinog/anaconda3/lib/python3.7/site-packages/keras/utils/losses_utils.py", line 35, in validate raise ValueError('Invalid Reduction Key %s.' % key) ValueError: Invalid Reduction Key auto.

    opened by barvinog 2
  • Chapter 2 convolution.py

    Chapter 2 convolution.py

    Hello Dominik, I'm a Python novice, but an experienced chess player and long ago a developer of software for infinite dimensional optimization. I've installed the latest Python on a 64 cores Ryzen Threadripper with two NVIDIA 3090 graphic cards. I study your very helpful overview of modern chess engine programming and started with Chapter 2 where except convolution.py all examples work fine. I have installed module scikit-image as skimage doesn't load correctly. Then (without changing the source of convolution.py) I get the following warning

    PS C:\Users\diete\Downloads\neural_network_chess-1.3\chapter_02> python.exe .\convolution.py (640, 480) Lossy conversion from float64 to uint8. Range [-377.0, 433.0]. Convert image to uint8 prior to saving to suppress this warning. PS C:\Users\diete\Downloads\neural_network_chess-1.3\chapter_02>

    and after some seconds python exits without any more output. Help with this problem is kindly appreciated. Dieter

    opened by d-kraft 1
Releases(v1.5)
Owner
Dominik Klein
random code snippets, including the chess program Jerry
Dominik Klein
GitHub Repository
Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields"

NeRF++ Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields" Work with 360 capture of large-scale unbounded scenes. Sup

Kai Zhang 722 Dec 28, 2022
Scheduling BilinearRewards

Scheduling_BilinearRewards Requirement Python 3 =3.5 Structure main.py This file includes the main function. For getting the results in Figure 1, ple

junghun.kim 0 Nov 25, 2021
Comp445 project - Data Communications & Computer Networks

COMP-445 Data Communications & Computer Networks Change Python version in Conda

Peng Zhao 2 Oct 03, 2022
Point Cloud Registration using Representative Overlapping Points.

Point Cloud Registration using Representative Overlapping Points (ROPNet) Abstract 3D point cloud registration is a fundamental task in robotics and c

ZhuLifa 36 Dec 16, 2022
TorchXRayVision: A library of chest X-ray datasets and models.

torchxrayvision A library for chest X-ray datasets and models. Including pre-trained models. ( 🎬 promo video about the project) Motivation: While the

Machine Learning and Medicine Lab 575 Jan 08, 2023
A TensorFlow implementation of FCN-8s

FCN-8s implementation in TensorFlow Contents Overview Examples and demo video Dependencies How to use it Download pre-trained VGG-16 Overview This is

Pierluigi Ferrari 50 Aug 08, 2022
Evaluation toolkit of the informative tracking benchmark comprising 9 scenarios, 180 diverse videos, and new challenges.

Informative-tracking-benchmark Informative tracking benchmark (ITB) higher diversity. It contains 9 representative scenarios and 180 diverse videos. m

Xin Li 15 Nov 26, 2022
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks Contributions A novel pairwise feature LSP to extract structural

31 Dec 06, 2022
CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields

CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields Paper | Supplementary | Video | Poster If you find our code or paper useful, please

26 Nov 29, 2022
Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun

ARAE Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun https://arxiv.org/abs/1706.04223 Disc

Junbo (Jake) Zhao 399 Jan 02, 2023
Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

Offline Meta-Reinforcement Learning with Advantage Weighting (MACAW) MACAW code used for the experiments in the ICML 2021 paper. Installing the enviro

Eric Mitchell 28 Jan 01, 2023
Paddle pit - Rethinking Spatial Dimensions of Vision Transformers

基于Paddle实现PiT ——Rethinking Spatial Dimensions of Vision Transformers,arxiv 官方原版代

Hongtao Wen 4 Jan 15, 2022
Code for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in Deep Latent Space"

SRHEN This is a better and simpler implementation for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in

1 Oct 28, 2022
PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC

DeepLab with PyTorch This is an unofficial PyTorch implementation of DeepLab v2 [1] with a ResNet-101 backbone. COCO-Stuff dataset [2] and PASCAL VOC

Kazuto Nakashima 995 Jan 08, 2023
Small little script to scrape, parse and check for active tor nodes. Can be used as proxies.

TorScrape TorScrape is a small but useful script made in python that scrapes a website for active tor nodes, parse the html and then save the nodes in

5 Dec 04, 2022
TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently.

Adversarial Chess TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently. Requirements To run

Muthu Chidambaram 30 Sep 07, 2021
HiddenMarkovModel implements hidden Markov models with Gaussian mixtures as distributions on top of TensorFlow

Class HiddenMarkovModel HiddenMarkovModel implements hidden Markov models with Gaussian mixtures as distributions on top of TensorFlow 2.0 Installatio

Susara Thenuwara 2 Nov 03, 2021
Optimize Trading Strategies Using Freqtrade

Optimize trading strategy using Freqtrade Short demo on building, testing and optimizing a trading strategy using Freqtrade. The DevBootstrap YouTube

DevBootstrap 139 Jan 01, 2023
The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

ELSA: Enhanced Local Self-Attention for Vision Transformer By Jingkai Zhou, Pich

DamoCV 87 Dec 19, 2022
A pytorch implementation of Reading Wikipedia to Answer Open-Domain Questions.

DrQA A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA). Reading comprehension is a task to produ

Runqi Yang 394 Nov 08, 2022