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Practical Neural Network Recipies in C++ (Paperback)

~ Masters (Author) "From the time of the first primitive computing machines, their designers and users have been trying to push computers beyond the role of automatic calculators..." (more)
Key Phrases: free non null, reset confusion, direc base, Eluding Local Minima, Arm Length, Preparing Input Data (more...)
3.8 out of 5 stars  See all reviews (20 customer reviews)
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Product Description

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.


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By Masters

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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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From the time of the first primitive computing machines, their designers and users have been trying to push computers beyond the role of automatic calculators and into the realm of "thinking" machines. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
free non null, reset confusion, direc base, previous err, int first child, prev err, simulated annealing local minimum escape, err trial error, resultant membership function, int popsize, int nvars, current err, delete sptr, int maxits, mid score, improved flag, tot err, key getch, step err, gen init, functional link network, start err, stopping temperature, few hidden neurons, input normalization
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Eluding Local Minima, Arm Length, Preparing Input Data, Relief Open, Strong Open
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3.8 out of 5 stars (20 customer reviews)
 
 
 
 
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25 of 25 people found the following review helpful:
5.0 out of 5 stars Very practical indeed, August 5, 2001
By W. A. Norris (Redmond, WA United States) - See all my reviews
(REAL NAME)      
This book is exactly as advertised. Other excellent books on Neural Networks will have you buried in mathematical notation that will challenge even readers with some statistics background and a couple of semesters of Calculus. These books are definitely worth your while if you can handle the math, but even then, translating these books from theories to solving real problems is no easy feat. By contrast, this book presents a good introduction to basic feedforward neural networks that is very readable to users with a moderate math background, and probably readable with some effort for motivated readers with limited math. You can read this book and come away with a reasonable understanding of how a feedforward network functions. Still, that's not even the strength of this book.

Not only is this book "practical" in the sense that it is readable, it is practical in that it tackles a host of additional topics necessary for using a neural network in the real world. It discusses annealing and genetic techniques for avoiding local minima. It discusses singular value decomposition for avoiding problems with redundant inputs. It discusses the best ways of building training sets and preparing input data, as well as ways of evaluating the performance of networks and attaching confidence measures. It would be easy to charge right in, use a neural network as a black box, give it a dataset and train it, and then wait for it to pop answers out. The only problem is, this will yield results that are worthless in the real world. All of these concerns have to be addressed to build a model that can actually be used for something.

I was very happy with the code base included with this book as well. In addition to a neural network using conjugate gradient descent (as well as Kohonen learning), code is integrated into the main program for annealing, genetic initialization, and singular value decomposition, as discussed in the text. I found the section on how to use the program slightly confusing at first, but once I figured out how to operate it, it was easy to set it up and use it. The code base is C++ that is deeply rooted in C, so it won't impress object-oriented gurus at all, but it should be understandable and fairly easy to work with for users with a good background in C, but who aren't C++ experts. For me, the bottom line is that the code works, it's not hard to understand (in my opinion), and it shouldn't be that hard to extend to perform new functions. In this day and age, it's probably worth mentioning that the program comes with a simple command-line interface, so if you want something that runs in a spiffy GUI, you'll have to write one.

I would recommend this book strongly as a first book on neural networks for readers that are interested in learning neural networks in the context of solving practical problems. I would also recommend this book to readers who have a book or two discussing the theoretical aspects of neural networks and want something that will help them translate that into attacking practical problems, and also provide a code base that will give them a head start.

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11 of 11 people found the following review helpful:
4.0 out of 5 stars A good start, May 23, 2000
By A. Scudiero (Minneapolis, MN United States) - See all my reviews
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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.
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10 of 10 people found the following review helpful:
5.0 out of 5 stars How to build 'em - How to use 'em - And actual source code!, February 13, 1997
By A Customer
Easily the best treatment of neural networks I have ever read. Outstanding treatment of the innards, how they work, and years of practical experience boiled down into heuristics for programming (with optimized source code examples!), configuring, training, and evaluating nets. The theory is brilliantly explained within each topical context in lieu of boring chapters on NN theory and math. Mathematical expressions are used only where they add clarity and are not gratuitiously used where the author's excellent English can do the job. And talk about English! Masters is one of those phenoms who speak math and English with equal facility. The writing is simply outstanding. The book is so good it is hard to decide what parts are most valuable. Amazingly, it is as useful for the novice wanting to learn something about neural nets as it is for a professional looking for tips and techniques! I have made the book mandatory reading for my team of knowledge discovery scientists and engineers
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Most Recent Customer Reviews

4.0 out of 5 stars The book delivers what the title says.
In contrast with many other books on AI and neural networks this book DOES give the following:

* limited amount of topics that are explained well. Read more
Published 15 months ago by Dekwo Kybon

5.0 out of 5 stars Excellent intro book for engineers and programmers
I am just starting to learn neural networks, and this book hit the spot. It is not overloaded with math, and its approach is very practical and well suited for... Read more
Published 20 months ago by T. Maly

4.0 out of 5 stars May be an intermediate level
This book isn't for a beginner. There're many good beginner's book for NN (or general AI), but this book delves into a bit more detail, and requires careful reading and... Read more
Published on August 23, 2007 by Anonymous from NJ, USA

5.0 out of 5 stars A great deal of Practical Advice
I bought my first copy of this book over two years ago. I wore it out, and I just ordered a new copy to replace it. Read more
Published on June 13, 2007 by P. D. Ryan

5.0 out of 5 stars The best book to learn Neural Networks
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... Read more
Published on June 9, 2006 by Sidhant

4.0 out of 5 stars best for beginnner
I found it is best for beginner with best suited examples. It teaches as if teacher is with you and the way examples seleted is the best. Read more
Published on August 9, 2005 by Dr. Sunil H. Ganatra

4.0 out of 5 stars helpful
I bought this book ages ago without any knowledge of neural nets and used the book and accompanying neutral.exe executable to conduct a research study. Read more
Published on July 15, 2005 by Alan Mead

1.0 out of 5 stars This book is a big lie
On a scale of 1 to 5, I wanted to rate this book as zero. On page 423, this book says "Complete code can be found on the accompanying disk." This is a big lie. Read more
Published on December 11, 2004 by Arcadio P. Sincero

4.0 out of 5 stars Complete C++ Source Code for Many Common Neural Network Algo
This book is exactly what is described by its title. It presents a cookbook of neural network recipes for the C++ programmer. Read more
Published on February 22, 2004 by Jeffrey Heaton

4.0 out of 5 stars Supurb practical text
I'll keep it brief. I've bought this book already a long time ago. And now and then still delve into it. Like many have said bfore me,it's exactly what the title says it is. Read more
Published on October 25, 2002

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