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

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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

Acerca del autor:

Max Pumperla is a Data Scientist and Engineer specializing in Deep Learning at the artificial intelligence company skymind.ai. He is the co-founder of the Deep Learning platform aetros.com.

Kevin Ferguson has 18 years of experience in distributed systems and data science. He is a data scientist at Honor, and has experience at companies such as Google and Meebo.

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