登入選單

返回Google圖書搜尋

Deep Learning and the Game of Go

Kevin FergusonMax Pumperla

出版	Simon and Schuster, 2019-01-06
主題	Computers / Data Science / Neural NetworksComputers / Programming / Games
ISBN	16383540149781638354017
URL	http://books.google.com.hk/books?id=CDszEAAAQBAJ&hl=&source=gbs_api
EBook	SAMPLE

註釋

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
1. Toward deep learning: a machine-learning introduction
2. Go as a machine-learning problem
3. Implementing your first Go bot
PART 2 - MACHINE LEARNING AND GAME AI
4. Playing games with tree search
5. Getting started with neural networks
6. Designing a neural network for Go data
7. Learning from data: a deep-learning bot
8. Deploying bots in the wild
9. Learning by practice: reinforcement learning
10. Reinforcement learning with policy gradients
11. Reinforcement learning with value methods
12. Reinforcement learning with actor-critic methods
PART 3 - GREATER THAN THE SUM OF ITS PARTS
13. AlphaGo: Bringing it all together
14. AlphaGo Zero: Integrating tree search with reinforcement learning