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An Introduction to Neural Networks
Get Free Ebook An Introduction to Neural Networks
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Product details
Hardcover: 248 pages
Publisher: CRC Press; 1 edition (August 5, 1997)
Language: English
ISBN-10: 1857286731
ISBN-13: 978-1857286731
Product Dimensions:
6.5 x 0.8 x 9.8 inches
Shipping Weight: 1.2 pounds
Average Customer Review:
4.1 out of 5 stars
7 customer reviews
Amazon Best Sellers Rank:
#569,029 in Books (See Top 100 in Books)
This is one of the best written books on NN. This book has that rare quality of being succinct but clearly written so that it can be understood by reasonably mathematical minded individual.It covers most of the basic topics (back propagation, feed forward, Hopfield nets etc) and gives idiosyncrasies of the field. It also gives you a simple but accurate understanding of the mathematics and algorithms for the field.I would highly recommend this book to anyone just entering into the field and has some mathematical background.
This book got me started with neural networks. Prior to this book I had only read some articles and didn't quite know what was going on. Now I have an application that makes football predictions straight up. It does not go overboard with math but there are certainly some deep sections. Even if you are using someone else's neural network objects, this is a good read to help you understand the concepts behind NN and what type you want to use.
It is a complete and precise description of ANN.I recommed this book for people looking for a good description in these topics.
Stopped reading after the last part of Chapter 2 on stochastic TLU with time, as the text becomes more and more unintuitive.
Great introduction.
Not deep in math (though there's enough to get you well started), but beautifully deep in concept. The topics do hit home, which wouldn't be the case if the author was showing off his/her academic prowess (as is TOO often the case nowadays). An excellent primer for the science/engineering/cognitive student to be introduced to the subject.
An excellent introduction to the subject. The author does a good job of presenting the core ideas in as intuitive a manner as possible without dumbing down the subject. Rigorous math is avoided making this an excellent introductory text for those wishing to grasp the fundamental concepts, and understand the power and practicality of neural networks. I would recommend this book as a companion to Simon Haykin's Neural Networks: A Comprehensive Foundation.
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