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am 16. Mai 2000
After reading the reviews I was really looking forward to reading this book, but ended up a bit disappointed. The editorial review and introduction lead me to believe that there was difficult material in the book, but I would be able to make my way through most of it.
I came at the book with a computer science background (and no prior neural network experience) and found the material rather difficult to follow. The statistics and math needed to really follow the book was more than I expected.
This doesn't mean the book is bad. After skimming through it a couple of times I really believe that the other reviewers are right -- this is a great resource on neural networks. However, just be sure you have the appropriate background to really get the most out of it.
If you are looking for an introductory book on neural nets or are a little rusty on your statistics and math I would recommend looking elsewhere.
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am 14. März 1997
This text has an extensive development of Neural networks
from a strong statistical basis. For anyone wanting a
quick way to access the broad spectrum of literature covering
neural networks and find the seminal papers, thoughts, developments
of the field, the literature references are worth the price.
This is essentially a literature survey, and not a "how-to"
book. It is not excessively heavy on the mathematics but
the uses verbiage to enhance the math that is necessary for
such a topic. It handles a number of significant but often
overlooked issues, such as the need for an ordering scheme of the partial
derivatives in backpropagation. Most authors don't address the obscure
but important points that will make or break your work if you
aren't aware of them. Ripley makes the reader cognizant of
where the minefields lie. This book is a Rosetta stone into
neural networks.
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am 13. Februar 2000
Neural Networks and the range of other techniques to come out of AI have never been given the statistical treatment that they need. Brian Ripley has created a book for a statistician such as myself that is informative and complete. It is interesting to see that many of the more traditional multivariate analysis techniques can be at least as good and better then some of the new AI versions. This book is a must have if NN and aligned technologies are to advance having some statistical measure of efficiency is essential
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