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
A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perform basic tasks.

AI_Personal_Voice_Assistant_Using_Python A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perf

Chumui Tripura 1 Oct 30, 2021
Locationinfo - A script helps the user to show network information such as ip address

Description This script helps the user to show network information such as ip ad

Roxcoder 1 Dec 30, 2021
The world's largest toxicity dataset.

The Toxicity Dataset by Surge AI Saving the internet is fun. Combing through thousands of online comments to build a toxicity dataset isn't. That's wh

Surge AI 134 Dec 19, 2022
Readings for "A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Therapy, and Drug-Drug Interaction Prediction."

Polypharmacy - DDI - Synergy Survey The Survey Paper This repository accompanies our survey paper A Unified View of Relational Deep Learning for Polyp

AstraZeneca 79 Jan 05, 2023
AFLFast (extends AFL with Power Schedules)

AFLFast Power schedules implemented by Marcel Böhme [email protected]

Marcel Böhme 380 Jan 03, 2023
Background Matting: The World is Your Green Screen

Background Matting: The World is Your Green Screen By Soumyadip Sengupta, Vivek Jayaram, Brian Curless, Steve Seitz, and Ira Kemelmacher-Shlizerman Th

Soumyadip Sengupta 4.6k Jan 04, 2023
A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features

2 Jul 25, 2022
A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

Deep Reinforcement Learning Agents This repository contains a collection of reinforcement learning algorithms written in Tensorflow. The ipython noteb

Arthur Juliani 2.2k Jan 01, 2023
We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

Deep GNN, Shallow Sampling Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, R

Facebook Research 117 Dec 20, 2022
A PyTorch implementation of the architecture of Mask RCNN

EDIT (AS OF 4th NOVEMBER 2019): This implementation has multiple errors and as of the date 4th, November 2019 is insufficient to be utilized as a reso

Sai Himal Allu 975 Dec 30, 2022
IJCAI2020 & IJCV 2020 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo

Seg_Uncertainty In this repo, we provide the code for the two papers, i.e., MRNet：Unsupervised Scene Adaptation with Memory Regularization in vivo, IJ

Zhedong Zheng 348 Jan 05, 2023
Near-Duplicate Video Retrieval with Deep Metric Learning

Near-Duplicate Video Retrieval with Deep Metric Learning This repository contains the Tensorflow implementation of the paper Near-Duplicate Video Retr

2 Jan 24, 2022
This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes Introduction This is the unofficial code of Deep Dual-re

chenjun 113 Dec 23, 2022
Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis

Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis, including human motion imitation, appearance transfer, and novel view synthesis. Currently the paper is under review

2.3k Jan 05, 2023
A Japanese Medical Information Extraction Toolkit

JaMIE: a Japanese Medical Information Extraction toolkit Joint Japanese Medical Problem, Modality and Relation Recognition The Train/Test phrases requ

7 Dec 12, 2022
This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021

Deep-Detail-Enhancement-for-Any-Garment Introduction This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in

40 Dec 13, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 05, 2023
MLP-Like Vision Permutator for Visual Recognition (PyTorch)

Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition (arxiv) This is a Pytorch implementation of our paper. We present Vision

Qibin (Andrew) Hou 162 Nov 28, 2022
Code for paper "ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation"

ASAP-Net This project implements ASAP-Net of paper ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation (BMVC2020). Overview We i

Hanwen Cao 26 Aug 25, 2022
A sample pytorch Implementation of ACL 2021 research paper "Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction".

Span-ASTE-Pytorch This repository is a pytorch version that implements Ali's ACL 2021 research paper Learning Span-Level Interactions for Aspect Senti

来自丹麦的天籁 10 Dec 06, 2022