Neural Networks Books

Any time you can pick up a used copy of a recently published technical book on an interesting topic at one-fourth of the retail price, you know there must be a problem. You would be right. This book tries to do three things at the same time and fails with at least two of its goals. It tries to talk about the business issues of biometrics, technical issues of specific biometric technologies (face recognition, speech recogniton, etc.), and finally machine learning techniques used to accomplish the biometric measurements. Only at this last goal do I think the book comes close to success, and even then only on a sketchy high level. The first seven chapters do an OK job of explaining machine learning techniques and give you some very instructive figures that are often lacking in academic textbooks, especially on neural networks. Also, these chapters do a pretty good job of explaining the equations involved. What's lacking, though, even in these early chapters, are some simple numerical examples or algorithmic steps that would give you some guidance on how to approach a task. When the book tries to make the leap to connecting the machine learning techniques to biometric authentication in a meaningful way such that a computer scientist could code up an algorithm, the book really falls on its face. There are some nice block diagrams of biometric systems, but no real details on algorithmic steps that would allow you to realize any of those blocks. Instead, there is quite a bit of verbage on the competition involved on building particular kinds of systems, and some rhetoric on possible pitfalls in specific biometric designs. However, with you standing there not knowing where to start with your design, this advice is really not very helpful.

I would say pass on this book and if you need to learn machine learning techniques, start with the older book by Mitchell entitled "Machine Learning". It talks about all of the machine learning techniques mentioned in this book, plus there are plenty of examples. Used copies are still relatively inexpensive, and its content is accessible and complete. As for biometric techniques, I've found the best books concentrate on one technique, such as fingerprint verification, and don't stray into other forms of authentication. The following is the table of contents:

Chapter 1. Overview
Chapter 2. Biometric Authentication Systems
Chapter 3. Expectation-Maximization Theory
Chapter 4. Support Vector Machines
Chapter 5. Multi-Layer Neural Networks
Chapter 6. Modular and Hierarchical Networks
Chapter 7. Decision-Based Neural Networks
Chapter 8. Biometric Authentication by Face Recognition
Chapter 9. Biometric Authentication by Voice Recognition
Chapter 10. Multicue Data Fusion
Appendix A: Convergence Properties of EM
Appendix B: Average Det Curves
Appendix C: Matlab Projects

The book serves as a luke warm introdution to neural networks. For the reader planning on applying the material in an industrial setting the book is far from sufficient. An average entry-level programmer could probably successfully code a couple of different types of neural networks as the book supplies nicely written pseudo-code for only couple types. As soon as the reader is interested in pursuing any kind of variation on these basic networks he hits a dead-end wall with 'references for further study' carved in the concrete.

For early undergraduate and advanced highschool students the text provides a great introduction to the field without wasting time on opinion and praising. Rather the reader can dive write into the heart of basic neural network algorithms and brief analyses of why they work and what they are good for.

I found this book to be confusing and difficult to understand. It was used for the text in an Intelligent Systems at my Uni, and I believe that it made an already difficult subject even more so.

The book would have benefitted from a better explanation, perhaps with more thoroughly explained examples and full source code for all the examples included.

This is not really a book for those who have problems with higher math, nor for those whose interest in the area of "Artificial Intelligence" is casual. It is not really even a good book for use as a text.

This book covers the complete field of computational intelligence in a limited number of pages. In addition, not only theory is covered but many practical examples are presented as well. I am afraid that the book fails to be the introduction to the topic that it claims to be (many formulae are presented without sufficient explanation, for example).

English is the author's second language, and his thoughts are difficult to follow. Terminology is outdated/mistranslated, and the book is not well organized.

On the other hand, i got it for 1/15 of the regular price, it was in great condition, and it got here quickly

The reviewed book is dedicated to an extension of the perceptron, which in its initial form is able to classify correctly only linearly separable patterns. Minsky and Papert have suggested in their seminal book Perceptron (published in 1969) that this serious shortcoming may be surmounted by two different ways: The first way was an introduction of the so-called higher-order input activities that are represented by products of single input activities (e.g. x1„ªx2), while the second way employed hidden neurons. Minsky and Papert have rejected both these simple and straightforward extensions of the perceptron theory mainly due to nonexistence of a proper learning algorithm. In the reviewed book is extensively discussed another alternative way how to generalized perceptron towards an ability to classify patterns that are not linearly separable. The idea is very simple, authors postulated that weight coefficients may be complex numbers and that a respective activation function is determined as follows:

f(z)=1 if 0<=arg(z) f(z)=0 if Pi/2<=arg(z)Then it is easy to demonstrate that XOR logical function is realizable by this extension of perceptron. The whole book consists of different extensions of the above simple idea that are able to realize more complicated Boolean functions (in particular the so-called k-valued Boolean threshold functions). The notion of linear separability is extended to the so-called P-realizable functions, then multi-valued Boolean threshold functions may be correctly realized. Moreover, it is demonstrated that an incremental perceptron learning may be modified to adjust complex weight coefficients, so that multi-valued Boolean threshold functions are realized. At the end of the book illustrative applications are presented that demonstrate an effectiveness of the proposed method (e.g. an associative memory for gray-scale images processing). The book is written in a highly sophisticated style employing mathematical concepts (e.g. group theory) unusual in neural networks. What we may accept from the book, that is substantial enough to be included in neural-network lectures? The two extensions of percetron to overcome a linear-separability block suggested by Minsky and Papert may be completed by the third possible extension based on complex weight coefficients. This is an interesting fact, but I would recommend the book only to a reader, which already knows neural networks really well, likes mathematics, and is specifically interested in perceptron learning.

In short, this is a good book for an experienced C++ programmer who has no experience in Neural Networks.

For somebody who has no experience whatsoever in Neural Nets, this book is a nice primer. It won't get you a Ph.D. (or an A in your undergrad AI class, for that matter), but if you've never been introduced before and want an overview that explores the concepts enough to get you started and whet your appetite for more, this book is a good place to start. It is short and easy to read, while still having enough substance to prepare you for more thorough books.

A major downfall of this book is, as others have mentioned, that the code provided is of poor quality. This book is definately not a good place to learn C++, the book contains some obvious mistakes like function definitions with no declaration, etc. Many more errors in the code are, I suspect, a result of the age of this book, it was written prior to the ANSI C++ standard: syntax and logical structures have changed significantly since 1992. It is, however, nothing a good coder can't handle.

Perhaps he really does understand linear separability and how it applies to the exclusive or problem in neural networks. However, Mr. Blum's ludicrous excuse for an explanation of this classic problem fails to demonstrate anything, including that he knows what he's talking about. Unfortunately this is an exemplar for the entire book.

Wiley should be ashamed for continuing to peddle this

This looked like a good book, with code listings for several neural net programs, and examples. But when you get down to the details, the examples lack clear explanations of how the data should be input, and the code listings are full of errors--functions are defined in one section of code, but are not declared in the classes they are supposed to be members of. There are several typographical errors, and portions of the code are out of order. A file is #included in one of the sections of code, but is not available among the listings. Overall, a shabby book

This book must be my worst investment. The code is full of mistakes. The theory side is even worse. I bought this book for the code, to see how one does implement neural networks in an object-oriented manner - after reading the book i knew how not to implement! BAD!!!

The book purports to teach object oriented programming AND neural networks, but does neither. (However, it does teach bad programming habits.) The text doesn't explain any of the math or theory beyond the bare equations and the code listing is EXTREMELY unreliable. I've had to make several revisions (variable names and types, function parameters, extra functions, etc) just to get it to compile. This book is NOT recommended

This book is a big disappointment. In the rapidly changing field of information technology, it is important to stay somewhat up to date with technology. Although the copyright date is 1997, most of the book has not changed since its first edition (circa 1990?).

This book contains such gems as "Today the ARCNET protocol is widely used in a variety of LANS (Page 7-16)" Ha!

The authors also go into great detail about the wonderous SNA protocol, choices regarding terminals, etc.

TCPIP is listed as a 'Wide Area Networking Protocol' and is not listed under the LAN section.

Some 'recent updates' to the second edition talk about the Internet. The inform the reader about valuable Internet utilities such as Gopher and Archie?

According to this book and a question from the exam pool, Ethernet is used on bus networks only...not star.

This is the worst excuse for a technology book I have ever seen. They should rename it 'History of Data Communications'

I am using this book for a data communications class at a local technical school. This review refers to the Fourth Edition of the book, which is current at the time of this review (7 July 2005).

I agree with the last reviewer who said this book was out-of-date. The book devotes much discussion to hierarchical networks that use mainframes, front-end processors, concentrators, and multiplexors. I consider this to be acceptable since the book is meant for data communications classes and not networking fundamentals classes. Data communications texts should be expected to cover phone systems and evolutions of communications networks, in my opinion. However, the fact that the text discusses these types of networks as though they are the current norm is unacceptable and misleading to new IT students.

In addition, the book has several significant technical and grammatical errors in almost every chapter. Take the following example, which serves as a (false) example of CIDR notation for IP addressing:

"For example, the IP address 186.100.0.0 would appear as 186.100.0.0/20 in the CIDR system. The /20 in this example means that the first 12 bits are used to identify the particular network, leaving the rest of the bits to identify the specific host." (Page 12.12, fifth paragraph)

This example--the only example of CIDR given, and vital to understanding the system--is ABSOLUTELY BACKWARD! The /20 signifies that the first twenty bits of the address are used as the subnet mask, and that the last twelve are used for the host. Fortunately, I have studied for many technical certifications like the CompTIA Network+ and Cisco CCNA and was able to recognize this as I read it. Other students may not be so fortunate.

Here's another example, from the chapter on network security:

"Hackers often try to plant a Trojan Horse (a program that is designed to be hidden on the computer and then start at some predetermined time in the future to do some damage to the computer) or ..." (Page 10.13, second paragraph)

This is also absolutely false. The first part of the definition, that trojan horse programs are hidden, could be considered true, but that they start at a predetermined time in the future, and that they do damage to a computer are both completely false. The widely-accepted definition of a trojan horse is a program that performs a different or an addition function to the one it seems and purports to do. Furthermore, I don't know of a single trojan horse program that does damage to a computer when it executes. Usually, these types of programs open a port and run a daemon on a computer, or perform some other function to leave a security vulnerability. Any damage that results comes after an intruder compromises the system. A "time-bomb" is the common term for a program set to execute at a predetermined time.

This last example seems to me to be far more disturbing: not only is there a technical falsehood (or two) in the statement, it seems that the author(s) sincerely did not know the material. Shelly Cashman publishes a lot of texts for technical schools, and perhaps the authors were too concerned with meeting deadlines or including a comprehensive number of topics, even if they didn't necessarily have experience in these areas. Books from other technical publishers, such as O'REILLY, Deitel & Deitel, and No Starch Press would never consider such errors acceptable. The fact that these errors are still present in the fourth edition is disturbing.

What's more, there is no errata page at the Shelly Cashman website to inform readers of typos or errors in the text. In my opinion, this is absolutely unacceptable for any technical publisher.

Unfortunately, not having a great deal of experience in other areas of the data communications field, like telephony systems and older network architectures, I cannot at present recommend an alternative text, other than one that has a number of good reviews and comes from a more prestigious publisher.

Obviously the book was written by someone(s) whose English writing ability is very questionable. At many places of the book, the explanation was so poor that I had to google to get a clearer explanation. Many times I felt that the author(s) wrote the book without knowing what they were writing.

Obviously my review may sound too harsh. But if you read the first 10 to 20 pages of chapter 5, you may at least understand why I got to have such a low view of the book.The book needs to thoroughly re-written.

I purchased this book because I had previously used Thomson Course Technology in the past and it went very well. However, this book was very limited in regards to the information that I seen on the actual exam. The coverage of OSPF was limited and very short of being reliable. Furthermore, the discussion of discovery and packets was briefly discussed, whereas very important on the exam. On the other hand, the book was very easy to read and it explained a few aspects of Cisco that wasn't covered in other exam preparation books. For this, I gave it two stars. I would highly recommend this book to anyone that wants to understand the very basics of Cisco, but not for the person seeking their CCNA certification.

I had to purchase this book for a college course on Operating Systems. Save yourself the $ if you looking at purchasing a guide to Operating Sytems - there are better books on the market. But if you HAVE to purchase this book for college or university course, then use it like a reference. It is like reading a dictionary and will put you to sleep. The subject material is not well written, and is too vague, however if you have no prior experience with operating systems you might find it some what helpful as a guide on how to install Microsoft products, like Windows. It also covers Unix and Red Hat Linux Version 7.2, although very few pages are covered.
The authors have written this book like a a manual or technical guide to certain aspects of Operating Sytems. Do not attempt to read this book cover to cover, but only pull out the information that is relevant to you.

I had to purchase this book for a college course. This book is just like a dictionary on every possible bit of information you can think of about operating systems. Great book for Reference only. You won't want to read it cover to cover, but only read certain pages that is appealing and is relevant to what you are working on now. If you have absolutely no prior experience with Operating Systems, this book could be somewhat helpful. If you have some knowledge or advanced knowledge - use it for reference only.

I HAD to buy this book for a college course. If you want to know the basics about each and every flavor since Windows 95, you'll like this book. But his Linux knowledge is severely limited, and his knowledge of other Unix variants is even worse. Palmer & Waters have not used a unix machine in their lives and as a Unix admin I was quite disgusted with the ludicrous tripe they present, and I can assure you that much of it was wrong. While we get page after endless page on Microsoft domains, NIS, NIS+, and LDAP are entirely missing. The structure of each chapter is: 15 pages on W9x, WNT, WMe, W2K, WXP, W03, 3 paragraphs on Linux, 3 paragraphs on "Unix" which the category he uses to cover all non-linux variants, and 3 paragraphs on Macintosh.

To the authors: before you write a book on operating systems, get familiar with them. I could do better than you.

I would have given this book zero stars if it was possible.

This book would be more aptly named "A History of Microsoft Operating Systems: The Basics". As with another reviewer, I had to have this book for a course I took. There is little discussion of universal operating system fundamentals outside of file systems. The vast majority of the book focuses on MS operating systems. They spend more time talking about Windows 3.1 than Mac or UNIX. You want a book to help in troubleshooting or solving problems with operating systems? Unless you're a computer system neophyte this book won't help you. On the other hand, if you know little about operating systems and would like to learn the basics of how they work and the evolution of PC OSs, this would be a useful book.

The book tries to touch on a broad range of operating systems, but is not laid out very well, and alot of information contained is very broad.

While some of the information is valuable, alot of it can easily be discovered through the help files/manuals of the OS'es, or by using common sense. The diagnostic and troubleshooting aspects are mostly based on trivial common sense, and the troubleshooting software is limited to what comes with the OS (scandisk, defrag, regclean). The technology is a little behind in this book within regards to OS'es.

My recommendation is, unless you are taking a required course for college and need this book, dont buy it. Look for a book that's more focused on a certain OS (Windows, MacOS, Unix). Save your money.