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Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (Bradford Book)
 
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Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (Bradford Book) (Hardcover)

by RD Reed (Author)
5.0 out of 5 stars  See all reviews (1 customer review)
RRP: £48.95
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Product details

  • Hardcover: 352 pages
  • Publisher: MIT Press (31 Mar 1999)
  • Language English
  • ISBN-10: 0262181908
  • ISBN-13: 978-0262181907
  • Product Dimensions: 23.1 x 17.5 x 3 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon.co.uk Sales Rank: 922,035 in Books (See Bestsellers in Books)

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Product Description

Product Description

Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic ideas is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviours. This book focuses on the subset of feedforward artificial neural networks, with applications as diverse as finance (forecasting), manyfacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining MLP research in the 1990s.

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1 of 1 people found the following review helpful:
5.0 out of 5 stars The book is a five star effort., 19 May 1999
By A Customer
The book is a five star effort. Here is a review circulated in popular neural network newsgroup:

Newsgroups: comp.ai.neural-nets From: saswss@hotellng.unx.sas.com (Warren Sarle) Subject: Neural Smithing Message-ID: <F8wrMs.6Dp@unx.sas.com> Organization: SAS Institute Inc.

I have added a new book to the list of "The best elementary textbooks on practical use of NNs" in the NN FAQ (it may not show up on the server for a few days):

Reed, R.D., and Marks, R.J, II (1999), Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, Cambridge, MA: The MIT Press, ISBN 0-262-18190-8.

After you have read Smith (1993) or Weiss and Kulikowski (1991), Reed and Marks provide an excellent source of practical details for training MLPs. They cover both backprop and conventional optimization algorithms. Their coverage of initialization methods, constructive networks, pruning, and regularization methods is unusually thorough. Unlike the vast majority of

books on NNs, this one has lots of really informative graphs. The chapter on generalization assessment is a little weak, which is why you should read Smith (1993) or Weiss and Kulikowski (1991) first. There is a little elementary calculus, but not enough that it should scare off anybody.

One minor complaint: "smith" is not a verb!

Warren S. Sarle SAS Institute Inc. The opinions expressed here saswss@unx.sas.com SAS Campus Drive are mine and not necessarily (919) 677-8000 Cary, NC 27513, USA those of SAS Institute.

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