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Python Deep Learning

You're reading from  Python Deep Learning

Product type Book
Published in Apr 2017
Publisher Packt
ISBN-13 9781786464453
Pages 406 pages
Edition 1st Edition
Languages
Authors (4):
Valentino Zocca Valentino Zocca
Profile icon Valentino Zocca
Gianmario Spacagna Gianmario Spacagna
Profile icon Gianmario Spacagna
Daniel Slater Daniel Slater
Profile icon Daniel Slater
Peter Roelants Peter Roelants
Profile icon Peter Roelants
View More author details

Table of Contents (18) Chapters

Python Deep Learning
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Machine Learning – An Introduction 2. Neural Networks 3. Deep Learning Fundamentals 4. Unsupervised Feature Learning 5. Image Recognition 6. Recurrent Neural Networks and Language Models 7. Deep Learning for Board Games 8. Deep Learning for Computer Games 9. Anomaly Detection 10. Building a Production-Ready Intrusion Detection System Index

Training AI to master Go


The number of possibilities in chess, though vast, is not so vast that with a powerful computer, you can't defeat the world's greatest human player. Go, an ancient Chinese game whose origin goes back to more than 5,500 years, is far more complex. In Go, a piece can be placed anywhere on the 19 x 19 board. To begin with, there are 361 possible moves. So to search forward k moves, you must consider 361k possibilities. To make things even more difficult, in chess, you can evaluate how good a position is fairly accurately by counting the number of pieces on each side, but in Go, no such simple evaluation function has been found. To know the value of a position, you must calculate through to the end of the game, some 200+ moves later. This makes the game impossible to play to a good standard using min-max.

Figure 5

To get a good feel of the complexity of Go, it is worth thinking about how humans learn to play Go versus Chess. When beginners starts learning Chess, they make...

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