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About this book

Deep Learning and the Game of Go is intended to introduce modern machine learning by walking through a practical and fun example: building an AI that plays Go. By the end of chapter 3, you can make a working Go-playing program, although it will be laughably weak at that point. From there, each chapter introduces a new way to improve your bot’s AI; you can learn about the strengths and limitations of each technique by experimenting. It all culminates in the final chapters, where we show how AlphaGo and AlphaGo Zero integrate all the techniques into incredibly powerful AIs.

Who should read this book

This book is for software developers who want to start experimenting with machine learning, and who prefer a practical approach over a mathematical approach. We assume you have a working knowledge of Python, although you could implement the same algorithms in any modern language. We don’t assume you know anything about Go; if you prefer chess or some similar game, you can adapt most of the techniques to your favorite game. If you are a Go player, you should have a blast watching your bot learn to play. We certainly did!

Roadmap

The book has three parts that cover 14 chapters and 5 appendices. Part I: Foundations introduces the major concepts for the rest of the book.

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