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Very useful book to have if you plan to use NN (specially in matlab environment). The theory is very well covered and explained, along with graphical examples. The book lacks however, more insight into how to implemen NN in real world problems (for that, I would suggest seeing Fundamentals of NN by Laurene V. Fausett).
2 Hands down the best introduction
I knew the very poor Matlab Neural Network Toolbox User's Guide by the same authors and I was kind of expecting the same, and boy was I wrong!
This book is simply brilliant, a miracle of pedagogy. It is intended for undergrad classes, but it is so clear that graduate students will benefit enormously from reading it before any other material. Plainly put, this book makes you UNDERSTAND this difficult topic, more than any other book that I know of (Zurada, Smith, Hassoun, Haykin, Duda-Hart, Caudill, etc)
A selection of worked out problems are included at the end of each chapter, a practice that is highly beneficial but alas too rare in books of the kind.
I very much appreciated the very clear exposition of backpropagation, and optimization methods such as Levenberg-Marquardt.
A note to Matlab users: funky demos are available for free and illustrate the main points of the book.
3 Beale is brilliant!!!
I have been studying neural network design for almost 30 years now and I have never found a more enchanting text book than this one. From day one I could not put it down. In fact, I bought a copy for each member of my extended family. Beale is brilliant in the way he demostrates the design and capability of neural network systems. No one has ever captured the public's imagination and heart the way he does in this compelling work. He has taken the study of neural networks to the next level. Hence, the world will be a better place. H2BurBabes4Ever.
4 Good book. Period.
I purchased this book while looking for an appropriate textbook for use in my class on neural networks. This book is excellent for both beginners and experts. It is a rare book in that it demonstrates complex mathematical manipulations and principles (that are difficult to grasp and visualize - and explain) using examples. The review on mathematical principles is very useful. The book makes it easier to teach the subject now. Given the way everything is presented, this book will also help those that want to code their own networks. I recommend this book to everyone.
5 Easy Neural Network Design should be the title of this book
This book has an easy way the explain the complex of Neural Networks. Begins by a short resume first chapter that allows you understand the concepts and mathematical background of Neural Networks. Then you read chaptter after chapter and get deep and deep on Neural Networks. First you discover that they can clasify objects on diferent classes. After that you are notified that they can predict values based on historical data. And more interesting they can recognize patterns of objects. All of this with complete support of theory and mathematical explanations. This book is the perfect book for biliografy as theorical and practical background.
6 Great book - well done!
I have been fortunate to take Dr.Hagan's neural network class and use this book as the text. He is (in my opinion) one of the most understandable authors on this subject (which comes from his vast knowledge of the field). The book makes use of clear examples and informative diagrams. I may have skipped a few classes (sorry Dr. Hagan) but I had no problem at all learning the material from this book. I now write neural net (among other AI schemas) code on a regular basis and still use Hagan's book as a reference.
7 Excellent intro to NN maths but few practical advices
I read the entire book over a one-semester graduate course in NN. I was amazed by the quality of formalism (notation), which allow me to understand quite easily complex mathematical concepts, algorithms and proofs presented throughout the book. Authors introduced in an effective way all important mathematical concepts before using them. I felt this book is accessible for a beginner in NN field but you will need a good basis (one or more undergraduate courses) in linear algebra and calculus. Overall, this book constitutes an excellent introduction to NN but you will need an additional book to help you through more practical aspects of NN training. My suggestions are Chris Bishop (1995) Neural Networks for Pattern Recognition (chap. 8-9). or Reed & al. (1999). Neural Smithing : Supervised Learning in Feedforward Artificial Neural Networks.
8 Excellent book for understanding neural network innards.
I took a graduate neural networks course with Dr. Hagan who used this book. The book analyzes the contemporary algorithms for neural nets and shows why neural nets work (and don't work). MATLAB examples are on the supplemental disk but they can be coded easily in other languages. The convergence toward a solution is shown using 2D and 3D plots.