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Applying Neural Networks : A Practical Guide
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Author: Kevin Swingler
List Price: $70.95
Our Price: Click to see the latest and low price
ISBN: 0126791708
Publisher: Morgan Kaufmann (23 April, 1996)
Edition: Paperback
Sales Rank: 152,235
Average Customer Rating: 3.25 out of 5
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Customer ReviewsRating: 1 out of 5
A Request to the Book-Reviewers
I haven't read this book and I don't know much about NNS, but willing to start. I found the reviewers of this book did an excellent job. Of course, every book has some limitations. Those pointing out the limitations are requested to recommend alternative books, especially for the beginners. Please ignore the star rating by me because I have to fill this to post my view.
Rating: 4 out of 5
Not the deepest book on the subject
This book is a fairly easy read. About no mathematical blobs thrown around, and still it contains a great deal of information. While you will find truly deep books on neural networks, at least this is a book you will have a reasonable chance from start to finish.. And you will probably end up understanding most of it. Of course , it is an advantage to understand the backpropagation algorithm before buying this book (and also understand the math behind it), but it should contain all needed information. But be prepared to look at the references if you are going to implement a specific "not very standard" algorithm. Most of the papers are on the internet, so it shouldnt be a problem. The book only talks about feedforward and recurrent ANNs, using gradient descent seach ( Like backpropagation). It does not cover any unsupervised learning or GA training algorith. But if your field is supervised learning, this is a helpfull book for you. I havent looked at the software, and probably wont. If you want to truly understand ANN, implement the algorithms yourself.
Rating: 3 out of 5
Much more theoretical than practical
This book reads like a doctoral thesis. The neural network theory presented is quite complete, if difficult to wade through. Having "practical" in its title, I expected far better examples on the accompanying disk. However, the source code came with no make files and no sample data. Many syntax errors quickly became apparent when I tried to incorporate the code into a project (unmatched parentheses, use of undeclared variables, etc.). Once I fixed those, bugs in the code began to surface, such as closing the output file after calling "return" and other serious bugs. It is clear that the code has never been actually tested. To summarize, if you already know something about neural networks and want to get deeper into the theory and formulas, this may be the book for you. But it certainly will NOT get you started writing an NN application without considerable effort and additional research.
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