Object-Oriented Neural Networks in C++

Rating:
Summary: A very good book for those begining with Neural Networks.
Review: Unlike many books on similar topics, Object-Oriented Neural-Networks in C++ provides a truly object-oriented approach to the design and implementation of neural networks. This book is not only a good tool for those beginning with neural-networks giving step-by-step instructions for constructing a variety of neural network architectures, it also provides a good set of tools for the advanced neural network user to build more flexible and powerful neural-network architectures. The book covers 4 basic neural network architectures (ADALINE, Backpropagation, Self-Organizing, and BAM); however, the object foundation provided by the book can be the basis for any neural network architecture. The book contains a complete set of source code (in print and on disk) for all objects described, real-world examples, and a review of the C++ programming techniques used throughout the book

Rating:
Summary: If you are new to NN & know some C++ you need this book
Review: Well, this text isn't in any way a decent introduction
to the theory of Neural Networks. Beside explaining you that NN are built upon nodes and links and that nodes have values and links have weights, which you can probably figure out by yourself, it present NN rules and techiques without a word about why this is so. For example the formulas used to modify
the NN values during the training process are gives "as is", so
that you you must just copy them and use them without having a clue at what is going on. So why do I give it 4 stars? Because
it does have working source code, and not in some weird language
like Lisp but good old C++. C++ gurus might indeed have more than a grumble with the quality of the code, as it has some
glaring faults, both in style and concept (like attempting to inline virtual functions and poor distribution and organization of code among header and source files) but hey, it is very simple to understand, and it works! And that is already something especially for someone who is a beginner both in NN and C++. The NN described are ADALINE, Back Propagation NN, Self Organizing NN and Bidirectional Associative Memory, (in case this tells you something) but with a little work and study you can reuse part of the framework to create new architectures. I definitely prefer a book with working, explained code than a tome full of abstraction and algorithms whose implementation is left as an exercise to the (clueless) reader. At least with this book you can play with the code and see NN in action. To really understand what is going on though you will need a theory book. As a gentle introduction I recommend Fundamentals of Neural Networks, by Laurene V. Fausett