Download PDF An Introduction to Neural Networks, by Kevin Gurney
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An Introduction to Neural Networks, by Kevin Gurney
Download PDF An Introduction to Neural Networks, by Kevin Gurney
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- Sales Rank: #948180 in eBooks
- Published on: 2009-03-24
- Released on: 2009-03-24
- Format: Kindle eBook
About the Author
University of Sheffield
Most helpful customer reviews
24 of 25 people found the following review helpful.
A good introduction without serious math
By A Customer
I checked this book out of my university library and was pleasently suprised, although this is by no means a comprehensive book for those who are taking a high level course on AI/neural nets it serves as a very good and complete introduction to those of us who wish to learn more about a very interesting area of computing. After an introduction and review of notation, several basic models are introduced starting with the TLU and progressively presenting more advanced models.
The book is not aimed towards computer science students, and also has in mind other backgrounds. It does however require a sufficient background in science/math (basic algebra and geometry, vectors).
16 of 16 people found the following review helpful.
Concise but explanatory
By Shadman Zafar
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.
10 of 10 people found the following review helpful.
Just what I needed
By Marshal Dillon
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.
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