Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks By Russell Reed, Robert J Marks II

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Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
 By Russell Reed, Robert J Marks II

Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks By Russell Reed, Robert J Marks II


Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
 By Russell Reed, Robert J Marks II


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Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
 By Russell Reed, Robert J Marks II

  • Amazon Sales Rank: #1124293 in Books
  • Published on: 1999-03-26
  • Ingredients: Example Ingredients
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.00" h x 1.20" w x 6.00" l, 1.79 pounds
  • Binding: Hardcover
  • 352 pages

Amazon.com Review A practical book, Neural Smithing is aimed at the reader who intends to design and build neural networks for applications from forecasting to pattern recognition. The authors concentrate on multilayer perceptrons (MLPs) as the most commonly used neural network model, which adds to the book's overall clarity and focus.

This textbook-style reference begins with simple, single-layer networks and the elements of supervised learning. It then builds on these basics with such topics as error surfaces, genetic algorithms, and generalization. Examples and illustrations guide the reader through the discussion, but the authors don't suggest problems for further study--a small omission in an otherwise well-constructed book.

Readers must know calculus and statistics to make sense of the text, but they don't need much knowledge of neural computing. Whether used as an introductory textbook or as a professional reference, Neural Smithing is highly useful. Tightly focused and easy to use, it should have a place next to every neural toolbox. --Rob Lightner

About the Author Russell D. Reed is Research Assistant Professor of Electrical Engineering, and Robert J. Marks II is Professor of Electrical Engineering, both at the University of Washington.

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