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Practical Neural Network Recipes in C++ (with diskette)

 
Practical Neural Network Recipes in C++ (with diskette)   Author: Masters
By Morgan Kaufmann
Average Customer Rating: Average rating of 3.5/5Average rating of 3.5/5Average rating of 3.5/5Average rating of 3.5/5Average rating of 3.5/5

List Price: £50.99
Our Price: £66.48

Read more information about Practical Neural Network Recipes in C++ (with diskette) at Amazon.co.uk
Product Details
Binding: Paperback
Dewey Decimal Number: 006.3
EAN: 9780124790407
ISBN: 0124790402
Label: Morgan Kaufmann
Manufacturer: Morgan Kaufmann
Number Of Items: 1
Number Of Pages: 493
Publication Date: 1993-11-05
Publisher: Morgan Kaufmann
Studio: Morgan Kaufmann
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Customer Reviews

Average rating of 5/5Average rating of 5/5Average rating of 5/5Average rating of 5/5Average rating of 5/5 Fantasic introduction to neural networks., 1998-06-19
The author does a great job with this book. He presents the complex material of neural networks in a very simple manner making it understandable to anyone interested in: (1) finding out more about neural networks, (2) using neural networks in any field, (3) applying neural networks in any field of research (ie: medicine, biology, finance, etc...). The author goes over everything that one needs to know about neural networks -- from the basics to how to implement your own network. Not only does he present the material in a concise manner, but he also gives C++ code to implement a neural network both in the book and on disk. Overall, I think that this is an excellent book to begin with if you are interested in neural networks and their applications.

Average rating of 2/5Average rating of 2/5Average rating of 2/5Average rating of 2/5Average rating of 2/5 An alternative viewpoint, 2001-04-01
I must say that I have found this book very hard to get in to. The C++ listings are probably the worst C++ code I have ever come across with listings in a proportional font that makes them incredibly difficult to browse. The use of pointers is confusing ( I understand the need for speed etc) but honestly, functions with 18 parameters p149/50 (fn genetic) as an example. Where is the use of the keyword 'const' to at least give an idea of what variables are ins and which are outs etc. The technical content of the book seems good however (I confess that I have not read it from cover to cover for the reasons stated above) and has proved a useful reference on the subject. I am glad though that this was not the first book that I picked up on Neural Networks, as without doubt it would also have been my last.