Let's get a bit more details on exactly how much computation the min max algorithm has to do. If we have a game of breadth b and depth d, then evaluating a complete game with min-max would require the construction of a tree with eventual d b leaves. If we use a max depth of n with an evaluation function, it would reduce our tree size to n b. But this is an exponential equation, and even though n is as small as 4 and b as 20, you still have 1,099,511,627,776 possibilities to evaluate. The tradeoff here is that as n gets lower, our evaluation function is called at a shallower level, where it may be a lot less good than the estimated quality of the position. Again, think of chess where our evaluation function is simply counting the number of pieces left on the board. Stopping at a shallow point may miss the fact that the last move put the queen in a position where it could be taken in the following move. Greater depth always equals greater accuracy of evaluation.
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