- 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. 284.993 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, PhD, is a data scientist and indy publisher. Specializing in Python, R, Java and C#, he is an active technology blogger, open source contributor, and author of more than ten books. His areas of expertise include predictive modeling, data mining, big data, business intelligence, and artificial intelligence. Jeff holds a Master’s Degree in Information Management from Washington University and a PhD in computer science from Nova Southeastern University in computer science. He is the lead developer for the Encog Machine Learning Framework open source project, a senior member of IEEE, and a fellow of the Life Management Institute (FLMI).
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The author'a website provides downloads of all his software with liberal licensing.
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.
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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