Deep Learning and the Game of Go

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

1. 1. Toward deep learning: a machine-learning introduction
2. 2. Go as a machine-learning problem
3. 3. Implementing your first Go bot

PART 2 – MACHINE LEARNING AND GAME AI
4. Playing games with tree search

1. 5. Getting started with neural networks
2. 6. Designing a neural network for Go data
3. 7. Learning from data: a deep-learning bot
4. 8. Deploying bots in the wild
5. 9. Learning by practice: reinforcement learning
6. 10. Reinforcement learning with policy gradients
7. 11. Reinforcement learning with value methods
8. 12. Reinforcement learning with actor-critic methods

PART 3 – GREATER THAN THE SUM OF ITS PARTS
13. AlphaGo: Bringing it all together

1. 14. AlphaGo Zero: Integrating tree search with reinforcement learning