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 | | Fundamentals of Neural Networks: Architectures, Algorithms And Applications (Pie) by Laurene V. Fausett ISBN-10: 9780133341867 ISBN-10: 0-13-334186-0 ISBN-13: 9780133341867 ISBN-13: 978-0-13-334186-7 Hardcover 1993-12-19 Prentice Hall Find Lowest Price |
Editorials | ||
Product Description 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. | ||
Reviews | ||
Easy to read, easy to apply One of my students inspired me to dig deeper into the subject of neural networks which I knew next to nothing about in advance. I believe that some background in linear algebra (as is being taught in early university courses) is a necessary prerequisite as is of course some simple programming skills. But reading the book and applying the text is straightforward. Thus, I found the book to be a very suitable introduction - a breeze. | ||
Good introductory book, too expensive This is a really nice book. It's very clear, and shows the basics of Neural Networks. However, it's really just an introduction. Don't expect to get neural nets to work in practice using this book only! You should get more advanced books, and probably the help of someone with experienced in the filed if you want to actually use neural nets for hard real-world problems. The missing star is because of the price. It's too expensive for a small introductory book. | ||
The best book to get introduced to Neural Networks This is the book I used in my AI class. I have found it very well written and interesting to read and go through the very first neural networks models such as the Hebb net, the perceptron and the Adaline. Then the book continues by presenting simple neural network applications like pattern association. I remember that our professor did ask the class to do one of the proposed projects in the pattern association chapter which consisted of implementing a small OCR with a neural network and this exercise did really help to better assimilate the principles. Finally, the following chapters present other types of neural networks such as those based on competition and the very important backpropagation neural network. The only thing that you can complain about is its high price tag. For anyone interested in the AI field, it is recommended. | ||
Good, but not very mathematical This is an excellent textbook for beginners, giving a clear picture of what neural networks are, and where they are used. It also talks about back-propagation, associative neural nets, and more. But the biggest flaw is that the book has little mathematics. And it also doesnt have any working code (only pseudo-code). So if you are considering buying this as a textbook for a NN course you are taking at your university, well, I would suggest you take a good look at the book at your library before you decide to buy it. Most university courses put neural nets on a firm mathematical footing and might also have course projects that have to be done by the student. This book can help you with neither of these. And the book's pretty expensive, I really wonder why. | ||
Great Book This is a great book on the topic. The author approaches the subject from a practial point of view. There are good examples on how to use NN with real world problems (i.e.: using perceptrons for character recognition). A good reference to have. However I would not recommend trying to code the algorithms yourself, but rather use a NN package . | ||