Introduction to Neural Networks for Java, 2nd Edition (Paperback)

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Key Phrases: neural network results, incremental pruning algorithm, new matrix object, Neural Networks, Second Edition, Jeff Heaton (more...)

Editorial Reviews

Product Description

Introduction to Neural Networks with 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.

About the Author

Jeff Heaton is an author, college instructor, and consultant. Jeff lives in Chesterfield (St. Louis), Missouri. The author of seven books and over two dozen journal and magazine articles, Jeff specializes in Internet, socket-level/spidering and Artificial Intelligence programming. Jeff has worked with companies such as MasterCard, Anheuser-Busch and Boeing. A Sun Certified Java Programmer and a Senior Member of the IEEE. Jeff holds a Masters Degree in Information Management from Washington University in St. Louis.

Product Details

• Paperback: 440 pages
• Publisher: Heaton Research, Inc.; 2 edition (October 1, 2008)
• Language: English
• ISBN-10: 1604390085
• ISBN-13: 978-1604390087
• Product Dimensions: 9 x 7.5 x 1 inches
• Shipping Weight: 1.6 pounds (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon.com Sales Rank: #378,269 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

4 of 5 people found the following review helpful:

4.0 out of 5 stars Simple yet effective, February 1, 2009

By	Neuron (Finland) - See all my reviews

Amazon Verified Purchase

I happened to start reading the first edition of the book and realized quite fast that it was outdated, however after browsing Amazon for a while I decided to go with the same author, and I wasn't left disappointed.

This is a very good book for anyone starting learning Neural Networks. It might not give you everything in detail, but as far as giving a hands on approach to learning NN this is the book to read. If you, like me, happened to get the first edition I would recommend you to upgrade as well. This edition of the book is much more mature.

I would caution anyone that don't know object oriented programming that this book is based that. I bought the C# version of the book as well, and it doesn't seem any different than syntax wise.

1 of 2 people found the following review helpful:

2.0 out of 5 stars Lack of focus; doesn't deliver, October 13, 2009

By	Eric Hackman (Bay Area, CA, USA) - See all my reviews (REAL NAME)

Jeff Heaton undertakes the admirable task of writing a book that provides some background in neural network theory overlaid with a layer of practical Java coding how-to. Unfortunately, the book delivers on neither of these intentions particularly well. I have only a very basic background in neural networking (see "Neural Networks: A Comprehensive Foundation - 2nd edition" by Haykin) and was expecting Mr. Heaton to provide some theoretical explanation of why various network architectures are relevant to particular types of problems, how training algorithms differ from each other and why this is important...actually any bit of theory at all to shed some light on what the code was supposed to do. Instead, there's a brief chapter on matrix math, which in my opinion was not especially clear, and an occasional ball and stick diagram and almost no theoretical explanation of where any of it comes from or why it's important. I learned next to nothing about neural networking from this book, and what I may have learned is probably available on wikipedia in the space of a few paragraphs.

I could resign myself to the lack of neural networking explanation if the book instead presented a robust discussion of Java design as applied to neural networking architectures and algorithms. But, alas, this is not to be found either. The Java code is presented with no insight into the author's design decisions and therefore offers little in the way of educational material. Unless you are truly a Java novice, the code in the book will seem obvious and underwhelming. While it's apparent that basic neural networks can be constructed with relatively simple code, the author's failure to provide any explanation of code design or to frame the code within the context of a larger neural networking library perhaps results in the Java coding how-to portion of the book failing to deliver as well.

In short, I read the first 6 chapters of this book and decided not to waste any further time with it. If you want to understand neural networks, you won't find that here. If you want to learn to write Java code to build neural networks, you won't find that here either. You'll find code that the author has already written that you can use, but there won't be much educational value in it. The book truly is more of a user's manual or technical documentation for the author's neural networking Java classes and not much more. Perhaps that is useful if you want something simple you can drop into a project and run with. My suggestion to those who wish to learn and understand how to build neural networks in Java is to learn a little about the networks themselves then hack out some Java code yourself. You'll understand what the code means and be in a much better position to extend that code. And you'll definitely learn something along the way, which, unfortunately, I did not while reading this book.