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Introduction to Neural Networks with Java
by
Jeff Heaton
Introduction to Neural Networks in Java introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward backpropagation, Hopfield, and Kohonen networks are discussed. Additional AI topics, such as Genetic Algorithms and Simulated Annealing, are also introduced. Practical examples are given for
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Paperback, 380 pages
Published
November 25th 2005
by Heaton Research, Inc.
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Community Reviews
(showing 1-30 of 49)
This book is written well enough. I read more than I planned, but eventually I realized the author isn't good enough in math. Some of what he writes is very odd, like bipolar numbers. He's the only one using that term. I tried his demo and it sort of worked so there must be some basis to his equations. It makes you realize that Neural nets and genetic programming are really math.
This is a good introductory book on neural networks. Too many java code not related to neural networks, for example, presenting how to create some swing based user interface for Hopfield Network or Self Organising Map. I read it primarily because of using Encog framework (http://code.google.com/p/encog-java/), which is a neural networks library developed by the author of this book.
There's actually an online version as a free ebook here
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