Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (Hardcover)

To begin with, it seems reasonable to characterize "Neural Smithing" in broad terms. The book is not just a stuffy, hypothetical, academic treatment of Neural Networks loaded with formulas and references. It does contain these elements but they are encapsulated in a larger presentation which leads the reader on an adventure of exploration and an ultimately satisfying journey of discovery. The book is certainly well grounded in theory and motivated by classical approach but the overall message is: you too can make neural networks from scratch by following the principles, guidelines, suggestions, and hints presented in this handbook. What's more, your network will probably perform correctly, or at least you'll understand the reason why not.

"Neural Smithing" guides the reader through various channels and pathways, around pitfalls, and ultimately to an understanding of neural networks on a personal level. The reader comes away from the first reading with a feeling of intimate knowledge and intuitive understanding of neural networks. After this, the book transforms from a required travel guide into a trusty reference book. With over 380 references, it is a veritable who's who in neural network technology and a "must have" for any serious experimenters workbench.

The underlying assumption the authors seem to be working from is that the reader will in fact "Roll up their sleeves, sit down at a computer, and get involved with neural networks at a working level". Ultimately, they will arrive at the intersections of discovery marked by the various chapters of the book. Here, the information contained in the book stands ready to guide the "user" through these intersection in an interactive way where the reader participates in the choices. The reader is made to consider their particular application and the implication of a variety of choices on the results they are interested in achieving.

The authors bring a unique understanding and hands-on experience to their discussions. While the book is liberally referenced, the originality of the book is recognized at a higher level as the authors use their experience to facilitate overall integration into a coherent, comprehensive presentation. This book gets high marks for presentation, particularly in the areas of line-of-reasoning and chain-of-development. The style will be instantly recognizable to readers versed in formal, axiomatic development and presentation.

"Neural Smithing" is a sophisticated work replete with Hypercubes, Conjugate Gradient Descents, Correlation and Variance, Voronoi tessellation, and Genetic Algorithms. Yet least the reader begin to feel that they are in peril of being lost it conceptual hyperspace, have no fear. The authors have a down to earth (3-dimensional) way of pulling you back. "Just think of error optimization as a marble rolling around on hills and valleys," encourage the authors. Well, when you put it that way, how can you miss. Other euphemisms include "pin the hyperplanes to the data", and "leap-frog weights". The bottom line is that the authors have a way of bringing you with them even when the going gets thick.

Finally, I believe this work to be comprehensive. It is well paced from beginning to end and covers all the bases in a very logical format. First, the basics are presented in excellent depth. (I believe other books miss this opportunity in many cases). Next, the heart of neural network theory and operation is presented and classically motivated. Finally, more exotic topics are covered (in equivalent depth I might add) such as Genetic Algorithms as they apply to neural networks, and generalization heuristics. The discussions are extremely well documented with references, formulas, and very relevant figures. Also, the appendixes provided are excellent and pertinent.

"Neural Smithing" is a handbook, a reference book, a cookbook complete with recipes for getting the job done, and finally a documentary on the exploration of an exciting field which is clearly setting the foundations for eventual understanding of the human mind. I would strongly recommend this book to someone who feels a compelling need to have access to the tools of this realm. This book is a vehicle for exploration.

Frank S. Holman III, Boeing Commercial Airplane Company, Seattle, WA. 98124-0346

The topics covered are reminicent to those discussed in part 2 and 3 of the Neural Network FAQ. In chapter 6, the relationships between learning rate, momontum, trainig time and learning modes are presented graphically. With this, it helps me to rule out and avoid learning parameters that are unlikely to improve the NN performance. This is especially important if the dataset is large and the NN program is implemented in Java.

If the aim is to develop a NN solution that will give you the best results, I find both chapter 7 (heuristics for weights initialization) and 16 (heuristics for improving generation) are esential and saves me a lot of time from reading many journals.

In summary, this book has helped me to develop the art of NN optimization. It shows me how to visualize decision surface and the various graphical relationships between learning paramters and various components of NN topology. I think you will find this book very useful after your NN program is up and running and you are looking for ideas and explaination on how to improve the NN performance further.