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Python Deep Learning

Python Deep Learning

By : Zocca, Spacagna, Daniel Slater, Roelants
4.1 (10)
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Python Deep Learning

Python Deep Learning

4.1 (10)
By: Zocca, Spacagna, Daniel Slater, 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 (12 chapters)
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11
Index

Chapter 7. Deep Learning for Board Games

You may have read sci-fi novels from the 50's and 60's; they are full of visions of what life in the 21st century would look like. They imagined a world of people with personal jet packs, underwater cities, intergalactic travel, flying cars, and truly intelligent robots capable of independent thought. The 21st century has arrived now; sadly, we are not going to get those flying cars, but thanks to deep learning, we may get that robot.

What does this have to do with deep learning for board games? In the next two chapters, including the current one, we will look at how to build Artificial Intelligence (AI) that can learn game environments. Reality has a vast space of possibilities. Doing even simple human tasks, such as getting a robot arm to pick up objects, requires analyzing huge amounts of sensory data and controlling many continuous response variables for the movement of the arms.

Games act as a great playing field for testing...

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