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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 8. Deep Learning for Computer Games

The last chapter focused on solving board games. In this chapter, we will look at the more complex problem of training AI to play computer games. Unlike with board games, the rules of the game are not known ahead of time. The AI cannot tell what will happen if it takes an action. It can't simulate a range of button presses and their effect on the state of the game to see which receive the best scores. It must instead learn the rules and constraints of the game purely from watching, playing, and experimenting.

In this chapter, we will cover the following topics:

  • Q-learning
  • Experience replay
  • Actor-critic
  • Model-based approaches
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