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Practical Neural Network Recipies in C++, by Masters
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This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assumed and all models are presented from the ground up.
The principle focus of the book is the three layer feedforward network, for more than a decade as the workhorse of professional arsenals. Other network models with strong performance records are also included.
Bound in the book is an IBM diskette that includes the source code for all programs in the book. Much of this code can be easily adapted to C compilers. In addition, the operation of all programs is thoroughly discussed both in the text and in the comments within the code to facilitate translation to other languages.
- Sales Rank: #816771 in Books
- Published on: 1993-04-14
- Original language: English
- Number of items: 1
- Dimensions: .96" h x 7.50" w x 9.17" l, 2.01 pounds
- Binding: Paperback
- 493 pages
Most helpful customer reviews
5 of 7 people found the following review helpful.
Complete C++ Source Code for Many Common Neural Network Algo
By Jeffrey Heaton
This book is exactly what is described by its title. It presents a cookbook of neural network recipes for the C++ programmer. I have used this book often, as I have developed a number of C++ and Java based Neural Network applications. The books is readable(at least as far as AI books go), it does not read like a mathematics text book, as many other AI books do.
The chapters are logically broken into the major neural network tasks: classification (identifying something), autoassociation (identifying a pattern by returning the same pattern), Time-Series Prediction (this is commonly applied to predicting the stock market, etc), Function Approximation.
As the author introduces these topics, various network architectures are discussed, such as feed-forward, multi-layer, backpropagation, and probalistic networks. Network optimization methods such as eluding local minima are tackled through the use of genetic algorism and simulated annealing.
16 of 16 people found the following review helpful.
A good start
By A. Scudiero
Some of the other reviewers of this book must have suffered from a misconception about the book. It is exactly as advertised, if you don't think so, compare it to Neural Networks, A Comprehensive Foundation by Haykin or Artificial Neural Networks by Schalkoff. Those are REALLY academic. Neural Networks is a very difficult topic,but this book does the best job I've seen yet of explaining Neural Nets in a Straightfoward, understandable way. C++ Neural Networks & Fuzzy Logic by V. and H. Rao tried this and failed. The math is very needed, and I respect the approach of only looking at one type of neural net (feedfoward 3 layer) in depth rather than a billion short, unexplained looks a many. Yes, the code is not the best I've ever seen, and it gets a bit rough to follow, but it explains the ideas. Overall I'd say know a little about what you're getting into before buying ANY book on Neural Networks.
14 of 14 people found the following review helpful.
The best book to learn Neural Networks
By Sidhant
As an undergraduate Math and Computing student, when I took up a high-level course in Neural Networks as an open elective, the only book my instructor recommended was Neural Networks by Simon Haykin. I got that book, and was soon put off by the mathematical rigour it had.
It was sometime later that I came across Practical Neural Network Recipes in C++ by Masters'. This, by all standards, is an exceptionally well written book.
It has the complete code for a neural network application, including Conjugate Gradient based back-propagation, Simulated Annealing and Genetic Algorithm powered optimisation, and much more. The code, although not very object-oriented, is clear and easy to follow. Undergraduates with a limited knowledge of mathematics will most certainly appreciate the way Masters' deals with the underlying concepts behind neural networks training and use. He simplifies the mathematical equations, and the code listings serve to see the math in action. The more mathematically mature can look into the excellent references provided in the text.
When much later in the course I went on to study Recurrent Networks (RNNs, which Masters' doesn't cover in his book), I found myself going back to Masters' when I had to implement algorithms for RNN training. This is one book that will teach you to convert complex mathematical equations into working code. Its a skill that is of much importance to most computational science students. This book is a must have for all neural networks students and practitioners alike.
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