Introduction to the Theory of Neural Computation (Santa Fe Institute Studies in the Sciences of Complexity) | 
 enlarge | Author: John A. Hertz
Creators: Richard G. Palmer, Anders Krogh
Publisher: Westview Press
Category: Book
List Price: $59.00
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Rating:  3 reviews
Sales Rank: 173322
Media: Paperback
Pages: 352
Number Of Items: 1
Shipping Weight (lbs): 1
Dimensions (in): 8.8 x 6 x 1
ISBN: 0201515601
Dewey Decimal Number: 006.3
EAN: 9780201515602
Publication Date: January 1, 1991
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Condition: 1991 PAPERBACK CLEAN PAGES VOL 1 327 PAGES
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| Editorial Reviews:
Amazon.com Review
This book comprehensively discusses the neural network models from a statistical mechanics perspective. It starts with one of the most influential developments in the theory of neural networks: Hopfield's analysis of networks with symmetric connections using the spin system approach and using the notion of an energy function from physics. Introduction to the Theory of Neural Computation uses these powerful tools to analyze neural networks as associative memory stores and solvers of optimization problems. A detailed analysis of multi-layer networks and recurrent networks follow. The book ends with chapters on unsupervised learning and a formal treatment of the relationship between statistical mechanics and neural networks. Little information is provided about applications and implementations, and the treatment of the material reflects the background of the authors as physicists. However the book is essential for a solid understanding of the computational potential of neural networks. Introduction to the Theory of Neural Computation assumes that the reader is familiar with undergraduate level mathematics, but does not have any background in physics. All of the necessary tools are introduced in the book.
Product Description
This book is a comprehensive introduction to the neural network models currently under intensive study for computational applications. It is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
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| Customer Reviews:
 Introduction to the Theory of Neural Computation October 6, 2000
Diana Thomas (Montclair, NJ)
8 out of 8 found this review helpful
This book is written from a mathematical perspective. The book introduces the Hopfield Neural Network with history and applications. The authors solve the network problem and develop the Hebb Rule. Links are made to Ising Spin models and stochastic problems. I find this book to be one of the best written mathematical guides for Neural Networks.
Clear and logical exposition August 18, 2007
Barry J. Wythoff (Newburyport, MA USA)
It's not the latest book on this topic, so today, there are other texts that have more recent developments to be sure. I originally read this text about 15 years ago. But what I got from this book, that I didn't get from most, are important insights and clear understanding of the material that's covered. The authors have a deep understanding, and have teaching as their goal in writing. Most other texts in this area are lacking in one or both of those characteristics, and aren't worth the paper they are printed on.
 A Broad Survey November 8, 1997
12 out of 14 found this review helpful
This was a good survey, and well-grounded mathematically. It is kind of scattershot, and if you primarily want to do practical projects like predicting financial markets, a lot of the sections won't be relevant. But if you want a broad-based approach, emphasizing a variety of network designs fro different purposes, this book is very good.
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