An Introduction to Neural Networks

by Kevin Gurney

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Genres: Artificial Intelligence, Computer Science, Science, Engineering, Mathematics

234 pages, Paperback

First published August 5, 1997

Reviews

[Felipe · Rating 4 out of 5]

I read this book back in 2007, so right now it must be a little bit behind the times. From this book I learned about multilayer perceptrons and backpropagation to an extent in which I could implement it in code.
Today there are numerous resources that help illustrating neural networks. I strongly recommend 3Blue1Brown series on the subject: https://www.youtube.com/watch?v=aircA...

[Demitkecil]

This book grew out of a set of course notes for a neural networks module given as part of a Masters degree in “Intelligent Systems”. The people on this course came from a wide variety of intellectual backgrounds (from philosophy, through psychology to computer science and engineering) and the author knew that he could not count on their being able to come to grips with the largely technical and mathematical approach which is often used (and in some ways easier to do). As a result he was forced to look carefully at the basic conceptual principles at work in the subject and try to recast these using ordinary language, drawing on the use of physical metaphors or analogies, and pictorial or graphical representations. I was pleasantly surprised to find that, as a result of this process, my own understanding was considerably deepened; I had now to unravel, as it were, condensed formal descriptions and say exactly how these were related to the “physical” world of artificial neurons, signals, computational processes, etc. However, the author was acutely aware that, while a litany of equations does not constitute a full description of fundamental principles, without some mathematics, a purely descriptive account runs the risk of dealing only with approximations and cannot be sharpened up to give any formulaic prescriptions. Therefore, the book introduced what author believed was just sufficient mathematics to bring the basic ideas into sharp focus.

[Ali Frz · Rating 2 out of 5]

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