Fundamentals of Neural Networks: Architectures, Algorithms and Applications

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.

An objective book. The author explains with details each network model. Includes a detailed structured language of each algorithm, that makes easy to one write a computer program code. With the step to step numeric examples, the written program may be checked to acuracy.

This book covers everything you need to know on neural nets. It covers all the main learning algorithms, architectures and activation functions. Don't think that you can find it all out on the net, you can't. If you don't understand neural networks, buy this book.

I'm a senior in a Mechanical Engineering undergraduate program, and am researching ANN's for a professor. I had almost no knowlege of ANN's, and had tried finding a good overview of the subject as well as a clear description of algorithms used in ANN's. After looking at three other books, I was relieved to find this one. Also, it's organizational structure is the most sensible I've seen.

This text explains the why and how for understanding neural networks, beginning with thier biological counterparts, where NN are used, why, and how. Detailed discussions on the Hebb, Perceptron, and Adaline pattern classification nets are provided, as well as fixed wieght competitive nets, Kohonen Self-Organizing Maps, Learning Vector Quantification, Counter Propagation, and Back Propagation, just to name a few. I received the specific theoretical foundation for neural net deployment on projects with confidence, and have referenced the work in breakthrough research on machine learning.Highly recommended for the serious researcher or scientist/engineer/analyst deploying neural networks.