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Neural Networks: A Comprehensive Foundation (2nd Edition)
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Author: Simon Haykin
List Price: $116.00
Our Price: Click to see the latest and low price
ISBN: 0132733501
Publisher: Prentice Hall (06 July, 1998)
Edition: Hardcover
Sales Rank: 44,663
Average Customer Rating: 4.33 out of 5
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Customer ReviewsRating: 4 out of 5
Well written and fairly comprehensive
Haykin's book is probably the most comprehensive compendium of traditional neural network theory currently available. I say "traditional" because historically neural networks developed within the field of computer science, only loosely inspired by actual neuroscience. Feedforward networks, backpropagation, self-organizing maps, PCA, and hierarchical machines fit into this traditional lineage. A second branch of neural networks, inspired more heavily by biology, have sought to model brain function and structure. Within this camp are network models such as adaptive resonance theory (ART), BCS/FCS, integrate-and-fire models, and a variety of others. Though this second branch of neural network theory has applications in pattern recognition, image processing, clustering, etc., Haykin barely mentions it. In other words, Haykin presents the material that computer scientists and engineers want to see, but skimps on the more biological side of the field. That being said, the material covered in Haykin is very well-presented, with clear mathematical notion and typesetting throughout. The book is accessible to graduates and advanced undergraduates. It should be on the shelf of every serious researcher, though workers in the biological sciences may want supplementary material. Computer scientists, mathematicians, and other engineers will not be disappointed at all.
Rating: 5 out of 5
I wish all books were like this.
Extremely concise, extremely complete. Every new page has a new concept or method. In the first chapter, I knew more than I did after reading two other books I bought on the subject.
I would suggest, however, not to use this as an introduction. It's a bit more rigorous mathematically than some others, so use it if you understand the concepts first. It will shine new insight onto the concepts you already know, but it will probably fail at teaching them to you from the ground up.
I suggest this for the experienced Artificial Intelligence experimenter.
And for the love of god, use Perl for your test programs! Writing C++ classes for artificial intelligence is wholly impractical!
Rating: 2 out of 5
Not for programmers
This book could be good for Electrical Engineers or Physicists interested in the field, but I really would not recommend it to researchers with background in Computer Science. The notations and everything are makes reading and following naturally harder for us.
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