- Paperback: 461 pages
- Publisher: Pearson; 1 edition (9 Dec 1993)
- Language English
- ISBN-10: 0133341860
- ISBN-13: 978-0133341867
- Product Dimensions: 23 x 17.9 x 2.6 cm
- Average Customer Review: 4.0 out of 5 stars See all reviews (1 customer review)
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Amazon Bestsellers Rank:
807,253 in Books (See Top 100 in Books)
- #2 in Books > Computers & Internet > Computer Science > Algorithms > Neural Networks
- #3 in Books > Computers & Internet > Computer Science > Algorithms > Network Algorithms
- #10 in Books > Computers & Internet > Computer Science > Networking & Security > Network Topics > Network Architecture
- See Complete Table of Contents
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Product Description
Product Description
An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind, the text features systematic discussions of all major neural networks and fortifies the reader's understanding with many examples.
An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind, the text features systematic discussions of all major neural networks and fortifies the reader's understudy with many examples.
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Customer Reviews
Most Helpful Customer Reviews
9 of 10 people found the following review helpful
Format:Paperback
This is a very good book to give an understanding of how the 4 main types of neural network operate. That is as much as i needed to know for my university module, but if you plan to go on to implement a network, then this book lacks the source code and examples to help you on your way.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
12 reviews
22 of 22 people found the following review helpful
Solid and Complete
14 Dec 1999
Format:Paperback
This text explains the why and how for understanding neural networks, beginning with thier biological counterparts, where NN are used, why, and how. Detailed discussions on the Hebb, Perceptron, and Adaline pattern classification nets are provided, as well as fixed wieght competitive nets, Kohonen Self-Organizing Maps, Learning Vector Quantification, Counter Propagation, and Back Propagation, just to name a few. I received the specific theoretical foundation for neural net deployment on projects with confidence, and have referenced the work in breakthrough research on machine learning.
Highly recommended for the serious researcher or scientist/engineer/analyst deploying neural networks.
27 of 29 people found the following review helpful
Clear and Well Organized
27 Jun 2000
Format:Paperback
I'm a senior in a Mechanical Engineering undergraduate program, and am researching ANN's for a professor. I had almost no knowlege of ANN's, and had tried finding a good overview of the subject as well as a clear description of algorithms used in ANN's. After looking at three other books, I was relieved to find this one. Also, it's organizational structure is the most sensible I've seen.
11 of 12 people found the following review helpful
Good, but not very mathematical
16 Jan 2006
Format:Paperback
This is an excellent textbook for beginners, giving a clear picture of what neural networks are, and where they are used. It also talks about back-propagation, associative neural nets, and more. But the biggest flaw is that the book has little mathematics. And it also doesnt have any working code (only pseudo-code). So if you are considering buying this as a textbook for a NN course you are taking at your university, well, I would suggest you take a good look at the book at your library before you decide to buy it. Most university courses put neural nets on a firm mathematical footing and might also have course projects that have to be done by the student. This book can help you with neither of these. And the book's pretty expensive, I really wonder why.
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