- Taschenbuch: 440 Seiten
- Verlag: Heaton Research, Incorporated; Auflage: New. (1. Oktober 2008)
- Sprache: Englisch
- ISBN-10: 1604390085
- ISBN-13: 978-1604390087
- Größe und/oder Gewicht: 19 x 2,5 x 23,5 cm
- Durchschnittliche Kundenbewertung: 1 Kundenrezension
- Amazon Bestseller-Rang: Nr. 241.756 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Introduction to Neural Networks with Java, 2nd Edition (Englisch) Taschenbuch – 1. Oktober 2008
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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.
Über den Autor und weitere Mitwirkende
Jeff Heaton is an author, consultant, artificial intelligence (AI) researcher and former college instructor. Heaton has penned more than a dozen books on topics including AI, virtual worlds, spiders and bots. Heaton leads the Encog project, an open source initiative to provide an advanced neural network and bot framework for Java and C#. A Sun Certified Java Programmer and a Senior Member of the IEEE, he holds a Masters Degree in Information Management from Washington University in St. Louis. Heaton lives in St. Louis, Missouri.
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For self-study, "Elements of Artificial Neural Networks" by Mehrotra et al or an older book, "Fundamentals of Neural Networks" by Fausett, would be an excellent companion to Heaton, if you would like a combination of accessible theory with a working software suite that can be readily modified to solve real problems.
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.
This book limits it's scope to practice, which is fine by me.... If you want theory, feel free to look it up on wikipedia or one of the billion books on AI that only cover theory.
This book assumes you've read a little theory and jumps straight into practice: in it, the author walks you from hands-on from creating the basic neural nodes to creating and training simple decision nets, to building applications for predicting stocks moves and playing backgammon.
I've ready about neural nets, but was unsure on how to apply them in practical applications: this book clarified their design and usage. However, I will warn you, it's not an easy read, and requires you to have the code loaded on your computer nearby... this book is about practice, and the author pumps a lot of information out.
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.
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