Principles of Neurocomputing for Science and Engineering [Hardcover]

Fredric M. Ham (Author), Ivica Kostanic (Author)

Book Description

ISBN-10: 0070259666 | ISBN-13: 978-0070259669 | Publication Date: September 29, 2000 | Edition: 1

* Unlike other neural network books, this is written specifically for scientists and engineers who want to apply neural networks to solve complex problems
* For each neurocomputing concept, a solid mathematical foundation is presented along with illustrative examples to accompany that particular architecture and associated training algorithm
* Incorporates many detailed examples and an extensive set of end-of-chapter problems

Editorial Reviews

About the Author

Fred Ham is a professor at the Florida Institute of Technology.

Ivica Kostanic works at SAFCO Technologies Inc.

Product Details

• Hardcover: 642 pages
• Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (September 29, 2000)
• Language: English
• ISBN-10: 0070259666
• ISBN-13: 978-0070259669
• Product Dimensions: 9.5 x 7.4 x 1.3 inches
• Shipping Weight: 2.5 pounds
• Average Customer Review: 4.0 out of 5 stars  See all reviews (4 customer reviews)
• Amazon Best Sellers Rank: #2,242,501 in Books (See Top 100 in Books)

More About the Author

Fredric M. Ham is an author, a musician, a world traveler, a national and international speaker, and an endowed chair professor. He holds a Ph.D. in Electrical Engineering. His career started as a geophysicist at an oil company in Houston, Texas, and then he moved on to a large communications company in Florida where he worked on the Hubble Space Telescope and many other space-related programs. He has been a professor for 22 years. He teaches graduate classes, and is a researcher in the areas of machine intelligence, acoustics, digital image processing and biosensors. He has had a lifelong passion for riveting thrillers. He has written several short stories and is currently working on his next novel The Faculty Club. When he's not writing fiction he plays guitar in a ten-piece band.

Customer Reviews

Most Helpful Customer Reviews

9 of 10 people found the following review helpful:

5.0 out of 5 stars For beginners and graduate students, January 4, 2001

By 

Sungjin Park (Melbourne, FL USA) - See all my reviews

This review is from: Principles of Neurocomputing for Science and Engineering (Hardcover)

I strongly recommend this text for beginners and graduate students who want to understand Neural Networks. First, the text book explains the why, where, and how to apply Neural Networks, which is the most important point for understanding Neural Networks. Second, mathematical proofs, clear description of algorithms and MATLAB codes encourage me to do it myself. And a lot of mathematical foundation in the Appendix allow me to understand quite easily complex mathematical concepts.

5.0 out of 5 stars Best neural network textbook for beginners, January 1, 2009

By 

C. Chen (Taiwan) - See all my reviews
(REAL NAME)   

This review is from: Principles of Neurocomputing for Science and Engineering (Hardcover)

This is definitely a must read for all NN beginners. In my opinion, this book is very well organized. First, it puts theoretical stuff and applicaiton examples into separate parts (Part I&II). Second, in Part I, it starts with basic constituents of NNs: one neuron NN, activation fucntion, learning rule,...and then covers NNs of more large-scale types. Within each type, it describes the most rudementary or prototype NN first, supplements info regarding other variants next, and mentions the issues to be encountered for actual implemention. This all makes this book so easy to follow. I can quickily decide whether I can skip the coming section/subsection and no worry of discontinuity as I read along.

0 of 1 people found the following review helpful:

5.0 out of 5 stars For beginners and graduate students, January 4, 2001

By 

Sungjin Park (Melbourne, FL USA) - See all my reviews

This review is from: Principles of Neurocomputing for Science and Engineering (Hardcover)

I strongly recommend this text for beginners and graduate students who want to understand Neural Networks. First, the text book explains the why, where, and how to apply Neural Networks, which is the most important point for understanding Neural Networks. Second, mathematical proofs, clear description of algorithms and MATLAB codes encourage me to do it myself. And a lot of mathematical foundation in the Appendix allow me to understand quite easily complex mathematical concepts.