For a long time, the best results and the bulk of the research in AI's playing in video game environments were around genetic algorithms. This approach involves creating a set of modules that take parameters to control the behavior of the AI. The range of parameter values are then set by a selection of genes. A group of agents would then be created using different combinations of these genes, which would be run on the game. The most successful set of agent's genes would be selected, then a new generation of agents would be created using combinations of the successful agent's genes. Those would again be run on the game and so on until a stopping criteria is reached, normally either a maximum number of iterations or a level of performance in the game. Occasionally, when creating a new generation, some of the genes can be mutated to create new genes. A good example of this is MarI/O, an AI that learnt to play the classic SNES game Super Mario World...
Python Deep Learning
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Python Deep Learning
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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.
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Table of Contents (18 chapters)
Python Deep Learning
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Machine Learning – An Introduction
Neural Networks
Deep Learning Fundamentals
Unsupervised Feature Learning
Image Recognition
Recurrent Neural Networks and Language Models
Deep Learning for Board Games
Deep Learning for Computer Games
Anomaly Detection
Building a Production-Ready Intrusion Detection System
Index
Customer Reviews