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Neural Networks: A Comprehensive Foundation [Englisch] [Gebundene Ausgabe]

Simon Haykin
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Kurzbeschreibung

August 1998
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

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Produktinformation

  • Gebundene Ausgabe: 842 Seiten
  • Verlag: Pearson Education (US); Auflage: 2 Sub (August 1998)
  • Sprache: Englisch
  • ISBN-10: 0132733501
  • ISBN-13: 978-0132733502
  • Größe und/oder Gewicht: 23,2 x 18,2 x 3,8 cm
  • Durchschnittliche Kundenbewertung: 4.6 von 5 Sternen  Alle Rezensionen anzeigen (5 Kundenrezensionen)
  • Amazon Bestseller-Rang: Nr. 488.318 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
  • Komplettes Inhaltsverzeichnis ansehen

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Produktbeschreibungen

Synopsis

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

Buchrückseite

Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

NEW TO THIS EDITION

  • NEW—New chapters now cover such areas as:
    • Support vector machines.
    • Reinforcement learning/neurodynamic programming.
    • Dynamically driven recurrent networks.
    • NEW-End—of-chapter problems revised, improved and expanded in number.

    FEATURES

    • Extensive, state-of-the-art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology's capabilities and potential applications.
    • Detailed analysis of back-propagation learning and multi-layer perceptrons.
    • Explores the intricacies of the learning process—an essential component for understanding neural networks.
    • Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics.
    • Integrates computer experiments throughout, giving the opportunity to see how neural networks are designed and perform in practice.
    • Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary.
    • Includes a detailed and extensive bibliography for easy reference.
    • Computer-oriented experiments distributed throughout the book
    • Uses Matlab SE version 5.

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1 von 1 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Haykin's Neural Networks 29. April 2000
Format:Gebundene Ausgabe
Read the other reviewers below for more details and various viewpoints. Here I'm assuming that you will hire a reputable consultant or tutor to either translate the book into ordinary English more or less or to teach you the mathematics behind it. Neural networks are important for everybody to understand because this is one of the important directions that computers and robotics are taking: learning things. As you move into this book, you'll discover that there are important categories that such learning machines fall into: learning with a teacher (that is, with some examples for the machine to learn from) or without a teacher (with no such examples), also called supervised versus unsupervised learning. There's also learning without or with feedback (including subtypes of feedforward networks with short-term memory, associative memory, and recurrent networks which use input-output mapping or relationships). Even high school and college students who wonder why they have to learn statistics and probability may be astonished to discover that some of the most effective learning machines involve statistics and probability. They fall into various categories such as maximum entropy (literally maximizing the entropy), maximum likelihood (again, the idea of maximizing likelihood as used in everyday language is a rough approximation, though the mathematical one is much more precise), minimizing the energy (Hopfield networks), minimizing mean square error (literally minimizing squares of statistical errors, though there is more to it), etc. In the last category mentioned fall (mostly) Kalman filter-predictors, which I worked on at the Defense Department in the 1980s. Lesen Sie weiter... ›
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Von Ein Kunde
Format:Gebundene Ausgabe
An excellent book, explaining the "state of the art" in neural networks on a very high scientific level. The choice of subjects is actual and demanding. The chapters are well structured, leading the reader from easy to understand basic knowledge to high sophisticated contents. Formulas, diagrams, textual explanations and the "problems" at the end of each chapter are superior, and of high educational value.
With this book the reader can be sure to achieve an actual overview of the necessary and important fields of neural networks and neural computing.
This book is not only well suited for advanced students starting to get a comprehensive overview over the field of neural networks, but also for scientists already working in that area, to complete and update their knowledge.
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5.0 von 5 Sternen Theoretically Great 24. Mai 2000
Von "greg122"
Format:Gebundene Ausgabe
I found this book to be an excellent "research" reference. It's mathematical presentation is rigorous and provides good (up-to-date)theoretical foundation for the experienced scientist/engineer. Saying this, it is not a good book for the beginner especially when one only wants to know the general physical meaning of neural networks and where it is best applied.
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5.0 von 5 Sternen Informative and masterfully written. 14. September 1998
Von Ein Kunde
Format:Gebundene Ausgabe
A wonderfully well written, insightful, treatment of artificial neural networks. Beginning from the basics, the author sets forth both a technological and historical perspective for the understanding this multidisiplinary subject area. The book is written from a practical engineering perspective and comprehensively spans the entire discipline of modern neural network theory. A+
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2 von 3 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Hard to digest if you are not an engineer 10. März 2000
Von Mimosa
Format:Gebundene Ausgabe
I imagine this is a great book if you have a background in engineering. I took an engineering course with this book as the course text. Because I did not have the background, I struggled with the text and the problem sets.
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