Features
- Cover Type: Paperback with 146 pages
- Published by: IUniverse March 13, 2002
- Written in: English
- ISBN 10 Number: 0595219969
- ISBN 13 Number: 978-0595219964
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Book Dimensions:
9.2 x 6.1 x 0.4 inches
- Weighs: 8.2 ounces
Product Description
There is a deep desire in men, in order to reproduce intelligence and place it in a machine. Neural Networks are an attempt to reproduce the synaptic connections of our brain in a computer. Duplicating the way we use our neurons to think in a machine, it is expected to have a device that could be able to do ¡°intelligent¡± tasks, the ones reserved just to humans some time ago. Neural Network is a reality now, not a fantasy, and they have been made in order to recognize patterns (a face, a photograph or a song, are patterns) and forecast trends. I have seen many books about this subject in my life. All of them are hard to read, and tedious to learn, so I decided to make my own one.
For beginner readers, I have tried to use a simple language, in order to be understood by anyone who wants to know about nets. An easy to read, practical and concise work. If you are interested in the brain functions and how can we simulate it in a computer, you¡¯ll get here a differenty to penetrate into their secrets. For advanced readers who want to make their own nets, I have included a methodology for building neural networks and complete sample computer source-code with tricks that will save you a lot of time while designing it.
Reader Reviews
Most striking about the book is the poor grammar. True, we engineers are not known to be a linguistically elegant lot, but the grammatical errors in this text do serve as obstacles to any clear explanations of the topic. It should have been proofread by a native English speaker. Regarding the technology explained, the book misses the mark because it doesn't simplify the areas in which people truly have difficulty. Specifically, it doesn't clearly describe how error is "backpropagated" in the standard backpropagation neural net. That topic is the core of the subject and the book misses it. The author merely baby talks the easy parts of the subject and gives misleading pseudocode. There are many books that are much better at presenting simple, intuitive explanations of the theory. Lastly, the book assumes that backprop and Kohonen nets are "99% of artificial neural networks". That is an outdated view. Today, there are *many* types of networks (e.g., recurrent backpropagation nets, reinforcement learning nets, competitive learning nets, counterpropagation nets, neural gas nets, growing neural gas nets, etc.) None of these is addressed at all. Frankly, I would have been minimally satisfied with a clear explanation of the backprop and the Kohonen algorithms, but even that was lacking. I give it the second star only because the goal was a worthy one.
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