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Summary: Extraordinarily well written and comprehensive
Review: Rarely do I encounter a book of such technical quality that also is a pleasure to read. Bishop moves through sometimes difficult topics in a clear, well-motivated style that is appropriate as both an introduction and a desktop reference on neural nets. Definitely on the "A list."Bishop chose to not include discussions on a number of topics that might have diluted his focus on pattern recognition (for example, Hebbian learning and neural net approaches to principal components analysis). I think that these choices greatly strengthened the integrity of his presentation. I would love to see an updated edition with a discussion of recent results in statistical learning theory, kernel methods and support vector machines.
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Summary: good book but pity is that it do not have a disk accompy it
Review: Strongly suggest the author include matlab scripts for his example and problem.
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Summary: It makes a difficult topic easy to understand
Review: The theories of NN and PR are quite difficult to understand. But this book makes them much easier. The author can explain the concepts without using too much formula. If other authors could follow his step then the life is much easier!
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Summary: Grows on You
Review: This book came out at about the same time as Ripley's, which has almost the same title, but in reverse. At the time, I liked Ripley's better, because it covered more things that were totally new to me. Then a friend said he had chosen Bishop for a course he was teaching, and I went back and reconsidered the two books. I soon found that my friend was right: Bishop's book is better laid out for a course in that it starts at the beginning (well, not quite the beginning--you do need to be fairly sophisticated mathematically) and works up, while Ripley's is more a collection of insights all at the same level; confusing to learn from. Bishop is able to cover both theoretical and practical aspects well. There certainly are topics that aren't covered, but the ones that are there fit together nicely, are accurate and up to date, and are easy to understand. It has migrated from my bookcase to my desk, where it now stays, and I reach for it often. To the reviewer who said "I was looking forward to a detailed insight into neural networks in this book. Instead, almost every page is plastered up with sigma notation", that's like saying about a book on music theory "Instead, almost every page is palstered with black-and-white ovals (some with sticks on the edge)." Or to the reviewer who complains this book is limited to the mathematical side of neural nets, that's like complaining about a cookbook on beef being limited to the carnivore side. If you want a non-technical overview, you can get that elsewhere, but if you want understanding of the techniques, you have to understand the math. Otherwise, there's no beef.
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Summary: NOT FOR BEGINNERS
Review: This book is not for beginners. It is heavy into the mathematical side of neural networks. I bought this book hoping to be able to take away the overall picture, a more conceptual overview, but my ignorance in math prevented that. In one sentence: Way over my head. I'll give it 5 stars because the people who could understand it seem to think the world of it :)
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Summary: Very good work
Review: This book is the best treatment of the subject. To really understand the content, it's necessary prior knowledge of probability theory, but not in depth. It is well illustrated and, more important, the topics are explained in manner logic and sweep. This work don't contains everything, but it's cool because it's readable and sufficient rigorous.
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Summary: Recomended book to read
Review: This is a recommended book to read for people who would like to read about statistics and maths. People with few knowledge about these sciences will find it a bit difficult to read.
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Summary: A Thorough and Rigorous Introduction
Review: This is a terrific book if you want to understand why neural nets work, and how to make them work. As advertised, it really goes into practical issues like preprocessing and generalization, which are easy to do halfheartedly, but are complex issues if you really want to get the best results. If I had to have only one book on neural nets, this would be it, no contest.
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Summary: Believe me -- there is no better book for beginners
Review: This is definitely the NN bible for beginners. I used it first in 1996 just after it came out and I still use it for reference. Reading some of the other reviews I saw that some people think there is too much math in the book -- that is not true -- the well explained math in the book is necessary to make the topic extremely clear.
Now 6 years later it would be nice to have a second, extended edition covering other successful NN related areas like recurrent NNs, PPCA, ICA, etc., also maybe some online adaptation techniques using Bishop's gift of being able to explain in simple words & math.
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Summary: An excellent book
Review: 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.
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