PDF Ebook 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
PDF Ebook Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, by Russell Reed, Robert J Marks II
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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 idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (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 the last ten years of MLP research.
- Sales Rank: #1682865 in Books
- Published on: 1999-03-26
- 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.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Most handled book on my bookshelf
By A. Kernytsky
After owning this book for many years and reaching for it many times, I decided that I at least owed the book a five star review.
Early in my graduate career I began working with neural networks and discovered this book in a electronic bookshelf available at my university. After printing chapter after chapter to read on subway rides home I ended up buying it for convenience. It gave me the background I needed to code up a basic artificial neural network in C++ and to then extend it to fit my needs.
The style of the writing is the perfect balance of enough detail to understand a concept or method without unnecessary wordiness. Each chapter covers an important aspect of neural network development and application - for exmaple, internode weight initilaization techniques - and acts a sort of mini-review of the most popular methods with a clear explanation of the pros and cons of each.
This is an excellent bookshelf addition for anyone who works with neural networks.
0 of 0 people found the following review helpful.
This is a fantastic book. It covers a wide variety of topics ...
By Mr.Moo
This is a fantastic book. It covers a wide variety of topics in neural network programming and construction and breaks everything down quite nicely. It's a majorly complex subject but this book does a great job of explaining things.
3 of 3 people found the following review helpful.
Not for the weak of heart
By Brian Vandenberg
I bought this 9 or 10 years ago when I became interested in AI, but my math skills were pretty weak at the time. I've since completed a CS & math degree, and I find this book easy to read, thoroughly interesting, and insightful.
Many of the questions I found myself asking while I read were soon answered as I read later sections of the book.
For those considering the purchase and unsure whether they can handle it, for much of the book a decent exposure to calculus will suffice. For a few chapters some exposure to ordinary differential equations would be wise. Numerical Analysis is probably a good idea as well.
An exposure to probability & statistics (not the freshman version) would help as well. The section on initialization techniques talks about various probability distributions when determining methods for initializing weights. If you don't care about the why's, it can be used as a reference for coming up with a scheme for weight initialization, but I find it handy to know why my code is doing something so I better know how to tweak it.
-Brian
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