Rating: 
Summary: Excellent mathematical reference of Neural Networks
Review: A good book if you are looking for learning mathematical teory of Neural Networks or set a parameters of comercial application. Not recommended for beginners
Rating:
Summary: Very formal and well presented
Review: Although this book is not for beginners, you can use it as a startup text as long as you can understand the math behind it. The contents are beautifully presented and with the expected detail and formalism of such a great book. As a software developer I also use other books that are more algorithm-centered, but this is the one I look for when I want to read a formal exposure.
Rating:
Summary: Just the right blend of intuition and mathematical rigor.
Review: Bishop cuts through the hype surrounding neural networks, and
shows how they relate to standard techniques
in statistical pattern recognition. He concentrates on feedforward
and radial basis function networks, which are the ones used most
widely in practice. This book is about as mathematical as
Hertz, Krogh and Palmer ("An Introduction to the Theory of Neural
Computation", 1991), but is probably easier to read, and is
certainly of more use to the practitioner. A real gem!
Rating:
Summary: An excellent introduction to pattern recognition
Review: Do not be put off by the title: this book is more about pattern recognition than neural networks. Of course it covers neural networks, but the central aim of the book is to investigate statistical approaches to the problem of pattern recognition. An excellent companion to "Duda & Hart". As other reviewers have said: you will need a reasonable maths or stats background to get the most out of this book.
Rating:
Summary: fine technical exposition
Review: I found the clarity of the math and technical aspects of pattern exposition to be extremely high. The more math, in particular statistics, one has the better, but still does an excellent job in explaining some of the basic concepts for those who have not had sufficient exposure to them. Certainly fundamental and I would consider a valid university text.
Rating: 
Summary: Disappointing from my point of view
Review: 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:
Summary: Disappointing from my point of view
Review: 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:
Summary: Excellent technical reference and tutorial
Review: I'd like to agree with previous reviewers. Note that you will need a good mathematical background (especially in statistics) to understand the content. However, the book is completely thorough in developing all the key concepts and really tries to give you insight into the meaning behind the equations. It's style is that of an undergraduate level textbook, but a very well written one. To use neural nets effectively, I think you need to have at least one book like this.
Rating:
Summary: Sheer pleasure.
Review: 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.
Rating:
Summary: Sheer pleasure.
Review: 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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