Book Image

Python Deep Learning

By : Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants
Book Image

Python Deep Learning

By: Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter Roelants

Overview of this book

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.
Table of Contents (18 chapters)
Python Deep Learning
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
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...