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Practical Neural Network Recipies in C++
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Author: Masters
List Price: $69.95
Our Price: Click to see the latest and low price
ISBN: 0124790402
Publisher: Morgan Kaufmann (31 March, 1993)
Edition: Paperback
Sales Rank: 59,108
Average Customer Rating: 3.58 out of 5
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Customer ReviewsRating: 4 out of 5
Complete C++ Source Code for Many Common Neural Network Algo
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 out of 5
Supurb practical text
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.
Rating: 4 out of 5
Good practical book on NN's
I found this book a very good introduction to NN's. In particular it does a good job of describing the foundations and pitfalls of NN's. I believe that the author probably has more of a practical grounding in NN's than most given his experience in the defence industry. I also have Rogers' book, Looney (Pattern Recognition Using NN's) and Masters second book. Sure the code in this is not OO but it is probably the most readable C you will ever get in a book, easy to compile and I didn't find many errors in the compilation (Borland CBuilder, GCC) however I did keep getting errors in Rogers' code (not declaring variables (CBuilder) ). If you can understand C you can read the code and understand NN's. The description of annealing is better than most geostatistical texts.As mentioned by others, running the programs takes a bit of working out and this is the main defficiency I find. Looney's and Rogers' books are more academic and I find slightly harder to follow. Rogers' introduces a description language for NN's which I didn't find useful. I find it easier to read the code and each chapter and that has given me a reasonable understanding of NN's. Looney and Rogers' cover different algorithms. Rogers' OO approach is good. I have used Looneys RBFLN and it works well for my applications. My interest in NN's is practical applications in the earth sciences and my reading/interest is based around this. I am a med level C/C++ programmer who writes command line hacks to do data work and use OO when I have the time to clean up code. The title of this book really sums up what it is all about (as do Carl Looney's and Joey Rogers' Titles !!)
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