Freeman and Skapura provide a practical introduction to artificial neural systems (ANS). The authors survey the most common neural-network architectures and show how neural networks can be used to solve actual scientific and engineering problems and describe methodologies for simulating neural-network architectures on traditional digital computing systems.
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Most Helpful Customer Reviews
4 of 5 people found the following review helpful:
5.0 out of 5 stars
Very Good book, if you are really interested on it.,
By A Customer
This review is from: Neural Networks: Algorithms, Applications, and Programming Techniques (Computation and Neural Systems Series) (Hardcover)
The book explains and deduces mathematically the working principle of many architectures, and presents commented examples (good ones) in structured English (very similar to Pascal). I used it as the basis for many NN's and felt quite satisfied in all cases.
3.0 out of 5 stars
Confusing, sometimes sloppy,
By Drakenman "RAS" (Monterey, CA USA) - See all my reviews
This review is from: Neural Networks: Algorithms, Applications, and Programming Techniques (Computation and Neural Systems Series) (Hardcover)
Freeman and Skapura have written a book that I use often as a quick reference, an initial peek at some type of neural network or another. But the book is often confusing (as in the opening remarks on ART with an unnecessarily complicated diagram) and sometimes just downright sloppy (as in the discussion of the input layer to counter-propagating networks where contradictory formulas are used to describe the input normalization scheme, once on p.216 and then again, differently, on p.235, with a difference that is even more confusing.
0 of 1 people found the following review helpful:
4.0 out of 5 stars
Very good book, but too many mistakes,
By lz "lz" (Italy) - See all my reviews
This review is from: Neural Networks: Algorithms, Applications, and Programming Techniques (Computation and Neural Systems Series) (Hardcover)
Although the first edition of the book (maybe the only one existing) has too many printed mistakes, it is a very good book on NN.
It explains in a clear and mathematical way a lot of different kinds of NN. It's a pity that it is missing of Genetic Algorithms applied to NN.
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