Deep Learning and the Game of Go, Max Pumperla, Kevin Ferguson

Summary

Deep Learning and the Game of Go

teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.

Foreword by Thore Graepel, DeepMind

About the Technology

The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot!

About the Book

Deep Learning and the Game of Go

introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios!

What's inside

Build and teach a self-improving game AI

Enhance classical game AI systems with deep learning

Implement neural networks for deep learning

About the Reader

All you need are basic Python skills and high school-level math. No deep learning experience required.

About the Author

Max Pumperla

and

Kevin Ferguson

are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo.

Table of Contents

PART 1 - FOUNDATIONS

Toward deep learning: a machine-learning introduction

Go as a machine-learning problem

Implementing your first Go bot

PART 2 - MACHINE LEARNING AND GAME AI

Playing games with tree search

Getting started with neural networks

Designing a neural network for Go data

Learning from data: a deep-learning bot

Deploying bots in the wild

Learning by practice: reinforcement learning

Reinforcement learning with policy gradients

Reinforcement learning with value methods

Reinforcement learning with actor-critic methods

PART 3 - GREATER THAN THE SUM OF ITS PARTS

AlphaGo: Bringing it all together

AlphaGo Zero: Integrating tree search with reinforcement learning

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