• Title Deep Learning and the Game of Go
• Author(s) Max Pumperla and Kevin Ferguson
• Publisher: Manning Publications; 1 edition (January 25, 2019)
• Permission: Free to read entire book online by the publisher (Manning), limited time every day.
• Paperback 384 pages
• eBook HTML
• Language: English
• ISBN-10: 1617295329
• ISBN-13: 978-1617295324
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Book Description

The ancient strategy game of Go (Weiqi) 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!

This book 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.

It 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!

• 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 is a data scientist at Honor, and has experience at companies such as Google and Meebo.

• Amazon

• Deep Learning and Neural Networks
• Python Programming
• Machine Learning
• Artificial Intelligence
• Data Science

• Deep Learning and the Game of Go (Max Pumperla, et al)
• The Book Homepage

• AlphaZero Implementation and Tutorial (Darin Straus)

• Deep Learning (Ian Goodfellow, et al)
• Learning Deep Architectures for AI (Yoshua Bengio)
• Artificial Neural Networks (Bernhard Mehlig)
• Neural Networks and Deep Learning (Michael Nielsen)
• Deep Learning Tutorials (LISA Lab)