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Title: Neural Networks for Pattern Recognition by Christopher M. Bishop ISBN: 0-19-853864-2 Publisher: Oxford University Press Pub. Date: 01 November, 1995 Format: Paperback Volumes: 1 List Price(USD): $65.00 |
Average Customer Rating: 4.89 (18 reviews)
Rating: 3
Summary: Disappointing from my point of view
Comment: I was looking forward to a detailed insight into neural networks in this book. Instead, almost every page is plastered up with sigma notation (which gets incredibly tedious after the first 5 chapters). The author relies on formulae too much to impart his obviously vast expertise onto the reader. I am an above average computer programmer, and I understand most of the concepts in this book, since I have come across them in my other research projects. But Bishop does very little to explain some of the important key concepts in this book. For example, why so much mileage can be gotten out of integrals and sequences in this field is mystifying. Practical knowledge if NN's are not only assumed but taken completely for granted!
I imagine that a great deal of knowledge can be gleaned from this compendious tome, but it is very hard reading.
Good use of graphics though. I just wish this book was twice as long, so he could have fitted in some more background info. Then and only then will this book be worth its £26 asking price in my opinion.
I just hope it comes in useful one day!
Rating: 5
Summary: An excellent book
Comment: When I came across this book, I had already read several on the subject, including Beale & Jackson (lightweight) and Haykin (middleweight)
For a reader unafraid of basic statistics and linear algebra, this is an excellent beginning book. For the math wary, I would say read a math-lite conceptual book first. This was a text book in my master's program, and I heard from students with a weak math background that they found it extremely challenging.
Bishop rightly emphasizes the statistical foundations of feedforward networks. This is a large subject in and of itself, and he covers it well. It provides an extremely solid foundation.
Neural dynamics via recurrence, Hopfield Nets, and many other topics outside or on the edges of feedforward networks are not covered.
I find many NN books are poorly written, imprecise, and have little content. This is one of the best books I have read on the subject.
Rating: 5
Summary: Sheer pleasure.
Comment: If you want a very good, intermediate introduction to pattern classification this book must be on your bookshelf. It even does a very nice job explaining the EM algorithm in a few pages! Basic calculus is all you need to understand the book. A must read.
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Title: Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, David G. Stork ISBN: 0471056693 Publisher: Wiley-Interscience Pub. Date: October, 2000 List Price(USD): $125.00 |
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Title: Fundamentals of Neural Networks by Laurene Fausett ISBN: 0133341860 Publisher: Prentice Hall Pub. Date: 15 January, 1994 List Price(USD): $106.67 |
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Title: Practical Neural Network Recipes in C++ by Timothy Masters ISBN: 0124790402 Publisher: Morgan Kaufmann Publishers Pub. Date: 01 March, 1993 List Price(USD): $69.95 |
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Title: Neural Networks: A Comprehensive Foundation (2nd Edition) by Simon S. Haykin, Simon Haykin ISBN: 0132733501 Publisher: Prentice Hall Pub. Date: 06 July, 1998 List Price(USD): $117.00 |
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Title: The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman ISBN: 0387952845 Publisher: Springer-Verlag Pub. Date: 09 August, 2001 List Price(USD): $82.95 |
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