Rating: 
Summary: A practical implementation of neural networks.
Review: It may suprise some people to learn that not all programmers are experts at higher math. I had gone through dozens of web sites and several books looking for a way to implement a neural network. All of these sites talked about network theory and analysis with lots of greek letters that meant nothing to me but none of them had any code. This book lays out object oriented code for four of the most common types of neural networks and explains every property and function in detail. If you are looking for a book on neural network theory this is not it. If you are looking for code samples that let you build basic networks and play with them yourself this book is perfect.
Rating:
Summary: A practical implementation of neural networks.
Review: It may suprise some people to learn that not all programmers are experts at higher math. I had gone through dozens of web sites and several books looking for a way to implement a neural network. All of these sites talked about network theory and analysis with lots of greek letters that meant nothing to me but none of them had any code. This book lays out object oriented code for four of the most common types of neural networks and explains every property and function in detail. If you are looking for a book on neural network theory this is not it. If you are looking for code samples that let you build basic networks and play with them yourself this book is perfect.
Rating: 
Summary: Abstract insight.
Review: My review of this book, will be much like the book itself.The book introduces you to basic concepts as any good book should. It follows up with a quick refresher on C++ topics which
will be used, and explains why. It hastily progresses to quickly
skim different types of nerual-networks in a review. My personal problem is the implimentation of these NNs.
The author does a good job of explaining what the types of NNs
are good for certain goals, but by the next NeuralNetwork topic, has failed to go anywhere with it. Overall there is no buildup or end goal with the book, just a detailed code elaboration of various NN concepts. Granted, taking
theory and explaining it into code terms must be a difficult task. For myself, I feel left with only a partial book. Its a good "introduction" text, but it lacks body to really do alot with it.
Rating: 
Summary: Organized and helpful
Review: Neural networks are pervasive in the modern business and research environment. The applications of neural networks are enormous and include fields such as financial engineering, network modeling, computational radiology, medicine, and brain modeling. For readers who know C++, this book gives a thorough overview of neural networks from the standpoint of the source code used to represent them. It is expected that the reader has had some exposure to neural networks, for no detailed discussion is given of their history or properties. The author attempts to stick to a purely object-oriented framework, and refrains from giving what he calls a "coarse-grained" approach to an object-oriented implementation of neural networks. The later according to him is merely a collection of C++ wrappers around existing C code. The base objects are the nodes and links of the neural networks, called the Base-Node and Base-Link classes in the book. The author admits this approach degrades performance, but design and development of complicated neural net architectures is enhanced, he argues. After a brief review of object-oriented programming in chapter 2, the author treats the neural network base classes in chapter 3. He defines a neural net as a 3-tuple consisting of a pattern set, parameters, and topology. The ADALINE, backpropagation, Kohonen self-organizing, and BAM neural-network architectures are treated throughout the book in chapters 4, 5, 6, and 7 respectively. The XOR problem is discussed as an example of learning with a backpropagation neural network, along with an example from lithology. Also, for those readers who are using compilers that do not support template classes, the author creates a generic backpropagation neural-network class. This class is constructed to make the creation of a back-propagation class more transparent. The only major problem with the book is the lack of examples illusrating the Kohonen and BAM neural networks and the lack of in-depth discussion of neural networks. For readers very familiar with neural networks and C++, this book should work well.
Rating:
Summary: Excellent book (from beginner to intermediate level)
Review: Some knowledge of C++ is essential, but classes are very well laid out, and code is easy to use and re-use. To an extent, it is too academic, and could be streamlined a bit for performance. More comprehensive examples would be a plus. Overall an excellent introduction to neural net programming. A follow-up on other neural net architectures would be greatly appreciated.
Rating:
Summary: Bad Code, Okay Book
Review: The book was instructive about Object-Oriented Neural Networks. The code is really bad, but I assumed it was because he wrote it first using STL, then was told that some people may not be able to compile it because they wouldn't know how to set up STL or because of compatibilities ... but then some things just seem lazy, like making every function inline in the hopes that inline would be "better" somehow. I thought the code could be forgiven if there were some UML diagrams or if there were references to patterns. The index was unusably tiny. I wished there was some place we could get code updates or other versions of the code. However, there aren't many cheap books in this area of expertise, so it is still worth looking through ... try re-writing everything in Perl or Java to get away from the bad code.
Rating:
Summary: Bad Code, Okay Book
Review: The book was instructive about Object-Oriented Neural Networks. The code is really bad, but I assumed it was because he wrote it first using STL, then was told that some people may not be able to compile it because they wouldn't know how to set up STL or because of compatibilities ... but then some things just seem lazy, like making every function inline in the hopes that inline would be "better" somehow. I thought the code could be forgiven if there were some UML diagrams or if there were references to patterns. The index was unusably tiny. I wished there was some place we could get code updates or other versions of the code. However, there aren't many cheap books in this area of expertise, so it is still worth looking through ... try re-writing everything in Perl or Java to get away from the bad code.
Rating:
Summary: Pretty good at OOP and NN
Review: This book does give a good introduction to programming neural networks in C++. The author has a good grasp of object oriented design and he shows how to exploit this in the construction of certain kinds of neural networks. The code in the book is flexable and fairly well laid out. I was able to port it to Java with relatively few alterations. I would recommend this book to anyone interested in starting to program neural nets. It probably requires a little background in C++, but the primer in the beginning of the book actually taught me something (I read this book a year ago when I was fairly new to OOP)!
Rating:
Summary: A great beginner's book on Neural Networks.
Review: This book is an excellent book for people with little or no background in Neural Networks. The description and name of the book is pretty much what you'll get. The book begins by giving a background in C++, from there they build the basic objects and theory needed for neural nets in general. Then Rogers proceeds to give a quick overview of each of the four neural nets that he covers in the book, in terms of mathematical definitions of those networks. Then the next chapters are dedicated to his implementation of each one of those four networks (Adaline, Backpropagation, Self-Organizing, & Binary Access Memory), including examples of how each one could be used. If you are looking on a book that goes into a large variety of neural networks, or that goes into the in-depth theory behind neural networks, then this might not be the book that you are looking for. Although Rogers gives a sufficient background and a firm basis of neural nets and C++ (you shouldn't get lost in the reading), his main purpose in the book is the actual programming implementation. The books main function (in my view), is to provide the reader with the tools on how to actually approach PROGRAMMING neural networks. My only complaint for this book was the extent of the examples that Rogers provided. The examples were limited in scope (and he mentions this) and detail. It would have been a little more helpful if more "application" information had been given; instead of just "programming" information. Still, though, the book is worth it if you want a strong basis on coding a neural network. All in all a great book, if you take into consideration the book's purpose. Rogers has a clean and clear writing style. He provides enough fundamental information throughout the book in terms of programming background and neural network background before proceeding to give the "meat" of the book. Christopher Sean Morrison
Rating:
Summary: A great overview of object-oriented neural networks.
Review: This book is really good for the any person who is interested in neural networks. The author describes the four types of neural networks and shows how they are implemented in C++. Also, he presents a practical use for each type of network. Even though he uses C++ code to implement the neural nets, I believe that only a general knowledge of C/C++ is necessary to understand the implementation. The author leaves out some of the more advanced material. He DOES NOT go into too much detail about all (or most) of the uses of each neural net -- he only presents a limited number of applications. He does not abstract into how each network can be adapted for other purposes. All in all, I believe that this is an excellent book that will show you simple neural net applications and explain how they are implemented using C++.
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