Introduction to Neural Networks for Java

by Jeff Heaton

Introduction to Neural Networks for Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots. All Java source code is available online for easy downloading.

Genres: Programming, Artificial Intelligence, Computer Science

440 pages, Paperback

First published November 25, 2005

Reviews

[Daniel Korzekwa · Rating 3 out of 5]

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.

[notv]

There's actually an online version as a free ebook here

[Ryanjustus · Rating 3 out of 5]

Good introduction to neural networks. I was glad that this edition didn't use the JOONE package.

[Frank Palardy · Rating 3 out of 5]

Some of what he writes is odd, but I tried his demo and it sort of worked.