Neural Networks Books

This is a book that can potentially help the person responsible for an intranet start-up or redesign. She/he may be internal responsible or external assisting consultant.

Pros:
A quick read; covers a lot of ground; will introduce you to the major issues and challenges in intranet development.
Cons:
For people who have been involved with IT/www projects, this book offers little new; several writers have contributed, and the result is an inconsistent flow with some redundancy; the text will cover so many topics (technology, project management, intranet concepts, extranets, mobile devices, usability, information architecture, and more), that a we never really get in depth.

Who will really benefit from this book:
Students, absolutely newcomers to the field, and those who, as bedtime reading, need a catch-up overview of the field.

the book is great, very well written. and many people were waiting for the first
rigorous text providing the reader with a global view of the subject.
there's just a problem of bad timing: due to the fast rate at which radically new results appeared recently and will keep appearing in the next several months, the book is already old before hitting the market.

Rather than describing the generalized structures and algorithms involved in neural networking etc, the author presents very specialized code examples. Most of the code deals with the graphical interface, which is unfortunate.
I was looking for a book that described C++ classes of neurons and networks, but did not find such things here. I can't recommend this book any less.

With an impressive title as that, you'd expect some solid code interspersed with good explanatory notes. Instead this admittedly older book utilizes C++ solely to employ a hopelessly obsolete DOS-based Borland Turbo Vision interface. C (no C++) is used to somewhat clumsily implement all the actual neural networking code. If you are well-versed in C -> C++ porting, and are willing to ignore all the references to Borland & set up your own UI using e.g. Visual Studio, you will be able to extract some useful knowledge from this book. But my advice is to pass it up in favor of other choices.

Except for the hiccup at the beginning of using Turbo C++ DOS GUI, develops into an average to better than average book. Worth buying, but don't expect too much C++ or object oriented code. Has a good mix between theory and applied code. Could be improved in a second edition.

This book is semi-OK. It was my the first book on neural networks. Programs descriptions helped me significantly to get feeling of the field. The book was useful source of C code to implement homework assignments in AI class, although I was forced to scan the source code and use OCR program (running neural networks as well!) to get the electronic version of the code. Applications are badly commented. Obsolete Borland interface is such a pain to get rid of. Math is inconsistent, so I read other books on ANNs before I started to understand something. Not the best choice for the first reading...

I'm a student majoring in Information Tech. I'm studying in ANN, FA, and GA subjects. I've read this book, but I've not the SUPPLEMENT DISK. So, if you have it, please send it to my email above.

The reviewer that wrote the poor and misleading review described the book perfectly.

In a 4 page passage of the book I found at least 10 errors involving contradictions between text, tables and figures. This becomes very frustrating if you are trying to reproduce the results Gately achieved--a task I have found impossible.

Gately also claims great success however he does not test his neural nets on a validation set of data. This is a fatal error in real trading. It is therefor impossible to determine if he was successful at all.

It is clear that Gately does not have a background in quantitative sciences, as he has little skill in accurately and completely reporting procedures and results.

The idea of the book and the trial and error approach is good but another author should write the second edition.

Great for intermediate level investors who want to explore neural networks as they apply to financial markets. Author sticks to the overview process. One case study is used to illustrate procedures and concepts. Work is reasonably paced. I recommend it as a good starting point for neural network analysis. Requires some background in financial markets and Excel. Could benefit from including a CD of examples and concept elaboration.

About the only positive thing I can say is that he has done a good job at capturing the feel of the trial and error process that is so necessary in developing a neural network model. That said, I could write a book about what's wrong with the book. There is so much poor advice and misinformation in the book, I would scarcely know where to begin. As I read it, I literally cringed at the so-called "information" being passed on to the unaware.

His errors includes poor sampling of test data, no validation data, poor data preparation, highly correlated inputs, small sample size, basing design changes in on insignificant changes in error, etc.

The extensive bibliography may be useful to some, but I would bet my net worth that it is not a list of books he has read. If you have a copy of Azoff's book on forecasting, you will find that Gately essentially cribbed the bulk of his bibliography from Azoff. The few remaining entries were cribbed from the bibliography found in the Neuroshell literature. The book is fairly heavy with hype for the Neuroshell product. (Not that it's a bad product.)

If you're thinking of buying this, I would recommend instead Master's Practical Neural Networks in C++. Doesn't matter if you don't program, it has plenty of good text, and most importantly, sound advice.

What does this book promise?

"Succinctly explains how neural networks function, what they can accomplish as well as how to use, construct and apply them for maximum profit."

Ok, so perhaps it should say .... An introductory text with basic definitions and a example of how to use a neural network to predict the S & P 500. But even on the grander promise this book delivers. The book is too basic for those previously familiar with NNs, but perfect for someone who merely would like to begin and fears the prospect of becoming too frustrated to finish. Gately points out potential snares and discusses how to overcome them and walks the reader through a project. He describes how to preprocess the data to ensure meaningful results.

I have followed his steps and have been very please with my project, this book and the value I received when purchasing it.

This book offers a lot and delivers very little. For one thing, it gives no theory of NN, instead it wastes one full chapter on the brain and NN's. The front page offers to show "Top techniques for designing and applying the latest.....", but this is false advertizing!. In short, VERY DISAPPOINTING!.

The explanations in the book and the software are very poor.
The software has many bugs that prevent it from be a useful tool. It is very, very, very clear the author did not check his work to make sure the software and the descriptions in the book matched. I tried several of the examples in the book (starting from the very first one) and found several key points that were at variance with the description in the text. Moreover, the text itself is incomplete to poor in general. I see what must be several editing errors (ie. cases where cut and paste were used but never taken out when new text was inserted).

I tried contacting the author via email to ask questions --- not available.

The good news is, I bought the book very, very, very cheap!
If you can find it for day ... at a book sale, it might be worth it to play around with, otherwise, forget it! Maybe it was dirt cheap because it is so bad.

I found this book to present a straightforward look into the complex world of neural networks. It is ideal for someone with little or no background in the subject yet covers sophisticated techniques as well. There were plenty of diagrams and many practical examples of how to use neural networks in the "real world." The software presents some challenges, however, given it predates Windows 98 and 2000. It seems to only work with US regional settings. Nevertheless, I was able to get it up and running and enjoyed the several examples and exercises described in the book. Overall, nicely written, an excellent introduction to the subject, and a good value.

I bought your book ISBN 0-387-98225-8 in Delft, The Netherlands. The book is good but the Software has many errorrs. Please send me a new copy of Simulnet that is your best

Mr. Rzempoluck covers neural network theory quite well, but be aware that the software is very poor! There seems to be little correlation between the book and software, leading to endless frustration. In my humble opinion, you would be better off purchasing one of the other Neural Network tutors out there. They cover the same ground and don't seem to have the obtuse approach that this text sometimes takes.

This is a very useful book. It starts by introducing very important and interesting techniques in optimisation i.e. genetic algorithms, simulated annealing, tabu search and neural networks. It continues by giving examples of how the techniques have been applied in various case studies. The book also contains code so that users have a head start in implementing the techniques described. This book is good for beginners because it describes the basics of the techniques. It is also suitable for more advanced researchers because the case studies provoke ideas for further work. In conclusion, this book is a useful addition to the bookshelf of any researcher interested in intelligent optimisation.

I bought this book because it has source code for simulated annealing, genetic algorithms, tabu search and neural networks.
I have used the three first source code and ... they are so buggy.
They're wrote in C but doesn't compile due to evident syntax errors (so evident, are they here so as to made these source code unusable ?).
The content of the book is not equilibrate (some metaheuristics aren't discussed thoroughly).
If the authors use there source code, I think evereything presented in this book is completely false.
Don't buy it, there are some better books to buy.

This book gave a superficial coverage on 4 optimization techniques in Chapter 1. Out of the 4 techniques, only Genetic Algorithm was explained slighty more in detail. The rest are merely short examples. That is about all you will get from this book (one brief Chapter on ALL 4 techniques).

What good is this book withut tradestation code. Eventually you will not use it and will set on your book shelf collecting dust. I suspect the author knows how to program and all other author like him. Topics studied in MS computer science software courses leave it to programers to write, since they are qualified than those who don't know how, But can do research ans sell a book to excited hope to be traders.

My interest in NN/GA started 3 years ago and I found this book. It covers some advanced methods for trading. But I am not sure whether they are really better than the traditional technical analysis. The results in this book could not prove it. And some authors didn't disclose their methods clearly. It seems to be very common among people who are using NN/GA/fuzzy logic in trading. So it is hard to know the reliability and accuracy of their results. And we cannot repeat their experiments. Personally, I won't use them in trading. I also gradually lose the interest in this area. I would like to know anyone really made profit by using them.

Discusses financial applications of neural networks, genetic algorithms and fuzzy logic. Is aimed at the beginner to intermediate level. Focuses more on data processing and application than on the actual building of neural nets. Several useful examples are given. Sections are written by experts in that particular field. This is usually advantageous except ocassionally where terminology may not always be consistent between sections. Overall a good book if you know how to write your own NN/GA/FUZZY program or have access to one.

This is far more of a survey than it should be. I expected far more in the way of technical/mathematical subject development than this book provides. Instead, the content is dominated by qualitative discussions about how to tune neural net parameters. This part is worth knowing (hence the two stars), but is not enough to justify an entire book.

The math of back propagation learning encompasses all of 4 pages of Ch. 6. Nearly all the rest of the book are broad-stroke "examples" of how people use a proprietary package (BrainMaker) to build nets for various applications. The book is practically an add for BrainMaker, which I guess is not surprising since the book is published by "California Scientific Software Press", the makers of BrainMaker. Anyone sense a teeeeny amount of compromised objectivity here?

Perhaps my biggest problem with the text is the lack of either implementation examples or code. Anyone who will be reading this will be hardcore CS types who can code, and what they are going to want to see are examples of a working NN and how to code one. What they aren't looking for are instructions on how to used a canned (and costly) closed-source software package.

There are much better books on the subject than this one; please, read those and stay away from this one.

A pretty good book if you want to learn about Neural Nets. The theory, the basic algoritms and a bit about what kind of net to use for what type of problem. All math and other things you need to know is also explained in the appendix. I recomend it for any one that want's to learn about neural nets.

This book is excellent introduction to theory and practical application of neural networks and fuzzy logic.
I know that for sure because I was totaly illiterate in that topic before I red it.
This book requires some prerequisite knowledge from reader.
Reader must have at least average undergraduate knowledge of discrete matematics,probability theory and matrix algebra.

I saw from previous reviews that some "good" C++ programmers expects to learn all that mathematics from one 500 pages book.
I think that would be impossible even for a perfect neural network.

This book is excellent introduction to theory and practical application of neural networks and fuzzy logic.
I know that for sure because I was totaly illiterate in that topic before I red it.
This book requires some prerequisite knowledge from reader.
Reader must have at least average undergraduate knowledge of discrete matematics,probability theory and matrix algebra.

I saw from previous reviews that some "good" C++ programmers expects to learn all that mathematics from one 500 pages book.
I think that would be impossible even for a perfect neural network.

Being a professional C++ programmer with a background in Applied Math I didn't like this book at all. It's written in a very annoying way: sometimes it sounds like its author is trying to sell the whole concept of NN and Fuzzy Logic to the reader instead of explaining how, when, and WHY fuzzy logic and neural networks work, how to train them properly, and what their limitations are. One characteristic example: author presents a list of companies using fuzzy logic in real systems but never gives any useful details about these systems.

The book is not for a "mathematician" since it often lacks precision, coherence, mathematical rigor, clarity, ... More often than not, you will find wordy explanations instead of simple formulas

It will displease a "programmer" too. The book's title is "C++ Neural Networks and Fuzzy Logic" so one may expect to find some well-thought and proven design ideas on how to implement NN and FL in C++ as well as a decent C++ library. Instead you will find just an amateurish C++ code (like anybody had any doubts that NN can actually be implemented in C++).

Numerous times author uses this "trick": he introduces new concept, delivers a couple of vague statements about the concept, and promises a better explanation later (in the following chapters, next series, etc).

How do you like this for definition: "STABILITY refers to such convergence that facilitates an end to the iterative process". You can find a lot of such "pearls" in this book.

Don't waste your time... There are better books

FYI: the book includes just a floppy disk instead of CD.

I'm sorry, this book is nearly useless. I'm a vetern C++ programmer and I tried to create a fuzzy logic system from the concepts and code in this book and it didn't work out at all. At first I blamed myself then I found some other fuzzy logic books and realized that no, this book just wasn't able to articulate the concepts in a meaningful way. The Neural net code was even worse. I wanted a cook book, do this, get a simple net, do this get a simple fuzzy logic system, now take what you learned and make a real one. Nope.

They do go over the AI terms and types of Neural nets and I did learn something by reading about it but not enough to justify the price of the book.

At first this book promises to offer a great deal for the reader, and the copious amounts of code therein support this impression. But once one actually begins working with the text, the book's shortcomings become all too apparent.

The authors do a lot of 'hand waving' at important concepts, almost as though the book was written as a companion piece to a course lecture. (Sometimes it really does read as though it's just a transcribed volume of professor's notes.) Numerous times I found myself stopping and thinking "wait, don't get off that topic yet! You've barely addressed its basics," and wondering if I had somehow missed something on the previous page or two.

The code supplied is abominably written, a Frankenstein hodgepodge of C and C++ intertwined. This code can be made to run with some work, but it could hardly be used as a sound basis for further development or experimentation.

You can derive good conceptual information out of this book, but it takes a lot of work. You really have to bludgeon your way through it, and that is no recipe for a successful educational text.

The content is interesting.
It tries to focus on the basic concepts behind the various components used in DWDM.
The problem is the numerous errors and the incoherence throughout the book:
- notation varies;
- errors are flagrant and should have been easely corrected with a simple review;
- some fondamental errors of physics: the photon does not a mass!!!! (p. 8)

Should be reedited.

The book is fine for a salesperson, but why it cost so much? As a pshysisist the book gives me a good reference on how to approach explaning some technologies to non PhD people. By all means not technical - childs play.

Too complicated with all the abbrevations and mathematical details not to understand for "non-scientists". Not everybody is an PhD!

Much better book is the following:
ISBN: 3895781746

The book has good coverage, and will leave the reader with a decent overview of what's out there, but very little understanding of anything. While that seems like the point of such a book, it doesn't really make the book useful. This isn't popular physics--anybody who needs an overview of DWDM probably needs a little more than this tiny book can offer.

The other reviews about the book's low editing quality were right on. It has the feel of something produced with a laptop, one weekend and an inkjet.

Only 70 of the 250 pages are dedicated to DWDM technology directly. Full of conflicting and ambiguous comments. For instance the section on Arrayed Waveguide Grating states in two consectutive comments: >AWG's are temperature sensitive. To eliminate thermal drift... Next Comment: >AWG's operating in the wide temerature range (0-85C) have been reported. Most topics are treated rather lightly. Seems like a book put together in a hurry to cash in on the recent "Fiber" goldrush.