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Title: Practical Neural Network Recipies in C++ by Masters ISBN: 0-12-479040-2 Publisher: Morgan Kaufmann Pub. Date: 31 March, 1993 Format: Paperback Volumes: 1 List Price(USD): $69.95 |
Average Customer Rating: 3.58 (12 reviews)
Rating: 4
Summary: A good start
Comment: Some of the other reviewers of this book must have suffered from a misconception about the book. It is exactly as advertised, if you don't think so, compare it to Neural Networks, A Comprehensive Foundation by Haykin or Artificial Neural Networks by Schalkoff. Those are REALLY academic. Neural Networks is a very difficult topic,but this book does the best job I've seen yet of explaining Neural Nets in a Straightfoward, understandable way. C++ Neural Networks & Fuzzy Logic by V. and H. Rao tried this and failed. The math is very needed, and I respect the approach of only looking at one type of neural net (feedfoward 3 layer) in depth rather than a billion short, unexplained looks a many. Yes, the code is not the best I've ever seen, and it gets a bit rough to follow, but it explains the ideas. Overall I'd say know a little about what you're getting into before buying ANY book on Neural Networks.
Rating: 4
Summary: Complete C++ Source Code for Many Common Neural Network Algo
Comment: This book is exactly what is described by its title. It presents a cookbook of neural network recipes for the C++ programmer. I have used this book often, as I have developed a number of C++ and Java based Neural Network applications. The books is readable(at least as far as AI books go), it does not read like a mathematics text book, as many other AI books do.
The chapters are logically broken into the major neural network tasks: classification (identifying something), autoassociation (identifying a pattern by returning the same pattern), Time-Series Prediction (this is commonly applied to predicting the stock market, etc), Function Approximation.
As the author introduces these topics, various network architectures are discussed, such as feed-forward, multi-layer, backpropagation, and probalistic networks. Network optimization methods such as eluding local minima are tackled through the use of genetic algorism and simulated annealing.
Rating: 4
Summary: Supurb practical text
Comment: I'll keep it brief. I've bought this book already a long time ago. And now and then still delve into it. Like many have said bfore me,it's exactly what the title says it is. A practical intro with plenty of readable source.If people think the theory side is a bit light,they're ofcourse right, but that is exactly what the author intended to do.This book delivers on what it promises,no more no less.You can actually get to work after reading it ;)
I would for instance recommend "Bishop, Neural networks for pattern recognition" to get a more solid foundation,(which admittedly is not a bad idea). All in all worth every penny/dollar/euro.
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Title: Object-Oriented Neural Networks in C++ by Richard Rogers ISBN: 0125931158 Publisher: Morgan Kaufmann Pub. Date: 15 October, 1996 List Price(USD): $39.95 |
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Title: Neural Networks for Pattern Recognition by Christopher M. Bishop, Chris Bishop ISBN: 0198538642 Publisher: Oxford University Press Pub. Date: January, 1996 List Price(USD): $65.00 |
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Title: Neural Networks: A Comprehensive Foundation (2nd Edition) by Simon Haykin ISBN: 0132733501 Publisher: Prentice Hall Pub. Date: 06 July, 1998 List Price(USD): $116.00 |
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Title: Neural, Novel & Hybrid Algorithms for Time Series Prediction by Timothy Masters ISBN: 0471130419 Publisher: John Wiley & Sons Pub. Date: 06 October, 1995 List Price(USD): $70.00 |
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Title: Applying Neural Networks : A Practical Guide by Kevin Swingler ISBN: 0126791708 Publisher: Morgan Kaufmann Pub. Date: 23 April, 1996 List Price(USD): $70.95 |
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