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Fundamentals of Neural Networks: Architectures, Algorithms And Applications, by Laurene V. Fausett
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Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.
- Sales Rank: #191250 in Books
- Published on: 1993-12-19
- Original language: English
- Number of items: 1
- Dimensions: 8.90" h x 1.00" w x 6.90" l, 1.65 pounds
- Binding: Paperback
- 461 pages
From the Publisher
An exceptionally clear, thorough introduction to neural networks written at an elementary level. Written with the beginning student in mind, the text features systematic discussions of all major neural networks and fortifies the reader's understudy with many examples.
From the Back Cover
Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Good read
By Annette M.
older book, but the concepts are still explained great.
7 of 7 people found the following review helpful.
Neural Networks 151. Don't let the math scare you
By Amazon Customer
I picked up this book as a slightly more advanced and technical introduction to neural networks than the pure applied programming or highly conceptual books out there. Hence the title "Neural Networks 151" instead of "Neural Networks 101". I was looking for something that provided a detailed, behind-the-scenes look at the theory and structure of neural networks without requiring a PhD in math. This book delivered. While there is a great deal of math, it's actually quite straightforward if you look closely. For the most part, the math is simply a representation of what's contained in the text. Reading the text allowed me to grasp the mathematical equivalent quickly and easily. This is distinct from books which put out 20 equations and only have a few lines of text, leaving you to painfully and slowly work through each equation to figure out what's going on (reaching for other textbooks and lots of coffee along the way). Does it help to have a great deal of mathematical knowledge ? Sure. Is it absolutely necessary ? Not in my opinion. E.g., "delta x" simply means "change in x". If that sounds familiar or straightforward, the book shouldn't be too difficult. I skimmed sections that weren't totally applicable to the problems I was looking at, but it was good to see the knowledge was there in case I needed to return to it later. It seems geared towards an instructional setting, but not at a 301 level. As an intermediate book that solidified my understanding and knowledge of neural networks, it was perfect.
32 of 33 people found the following review helpful.
The missing star is due to the book's price
By Shlomo Yona
I bought this book as a reference book during a graduate course I took in Neural Computation.
The book was clear and useful in presenting the topics, and more importantly, in presenting the algorithms in a clearn simple format which made it very easy to produce a computer program implementing these algorithms just by reading the book.
It was also useful by listing various things which have been done in literature to alter the algorithms for various purposes.
I suppose that people in the field of Neural Computation might find this book useful as an introduction book and also as a reference book (at least for its clear explenations and algorithms listings), but otherwise need more comprehensive books which cover a lot more math than this books does.
Actually, this is a good book for getting to know this dicipline for people who don't like to mess too much with calculus. Other books in this fiels contain more, a lot more, calculus in them, so I would also argue that this book is useful for people who want to understand the ideas, have a clear algorithm so its easy to implement, and at the same time, not worry about the math too much.
You don't see proofs here, at least not as much as I expected to, but I suppose that this follows the idea that this book is useful as a complementary book, rather than the authoritative book in the subject.
I only regret the high price of the book.
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