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Feedforward Neural Network Methodology (Information Science and Statistics), by Terrence L. Fine
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This decade has seen an explosive growth in computational speed and memory and a rapid enrichment in our understanding of artificial neural networks. These two factors provide systems engineers and statisticians with the ability to build models of physical, economic, and information-based time series and signals. This book provides a thorough and coherent introduction to the mathematical properties of feedforward neural networks and to the intensive methodology which has enabled their highly successful application to complex problems.
- Sales Rank: #3230112 in Books
- Published on: 1999-06-11
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .81" w x 6.14" l, 1.44 pounds
- Binding: Hardcover
- 340 pages
- ISBN13: 9780387987453
- Condition: USED - Very Good
- Notes: 100% Satisfaction Guarantee. Tracking provided on most orders. Buy with Confidence! Millions of books sold!
Review
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From the reviews:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"...Fine must be congratulated for a coherent presentation of carefully selected material. Given the diversity of the field, this represented a serious challenge. Again, Feeforward Neural Network Methodlogy is an excellent reference for whoever wants to be brought to the frontier of research. I enthusiastically recommend it."
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Most helpful customer reviews
2 of 2 people found the following review helpful.
Deep knowledge about Neural Networks inner workings
By Carlos Barragan Orbe
If you're serious about understanding how Neural Networks work (Feedforward NN), then this book is a must have. It is a one of a kind resource in terms of the mathematical width and depth that is contained related to the topic.
It goes deeply into the mathematical reasoning and theorems that make NN learn and provides some sample Matlab code that can add to your understanding on implementation.
This book is not for everyone, you need to like and understand calculus to some degree to be able to follow it. This is a great acquisition if your interest is to gain a graduate course level understanding on the subject.
3 of 3 people found the following review helpful.
Great for practical applications
By A Customer
This book provides a nice balance of math, examples, and MATLAB computer. It was of great help for me to understand and code up my own forms of ANNs. The example code provided is a really nice feature. It also puts a great deal of emphasis on relating statistics with ANNs.
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