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Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience)

Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience)

Unsupervised Learning: Foundations of Neural Computation (Computational
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Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience) Paperback - 1999

by Hinton, Geoffrey

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MIT Press, 1999-06-11. First Edition. paperback. New. 9.15x6.07x0.85. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience)
  • Author Hinton, Geoffrey
  • Binding Paperback
  • Edition First Edition
  • Condition New
  • Pages 418
  • Volumes 1
  • Language ENG
  • Publisher MIT Press, Cambridge, Mass.
  • Publication date 1999-06-11
  • Features Index, Table of Contents
  • Bookseller's Inventory # DADAX026258168X
  • ISBN 9780262581684 / 026258168X
  • Weight 1.32 lbs (0.60 kg)
  • Dimensions 9.15 x 6.07 x 0.85 in (23.24 x 15.42 x 2.16 cm)
  • Size 9.15x6.07x0.85
  • Age range 18 to UP years
  • Grade levels 13 - UP
  • Category Medical / Nursing
  • Library of Congress subjects Neural networks (Computer science), Neural computers
  • Library of Congress Catalogue Number 98-14784
  • Dewey Decimal Code 612.82
  • Quantity available 6

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Reader reviews for Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience)

From the publisher

Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

First line

What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments?

From the rear cover

This volume, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.

About the author

Geoffrey Hinton is Professor of Computer Science at the University of Toronto.

Terrence J. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at the University of California, San Diego. He was a member of the advisory committee for the Obama administration's BRAIN initiative and is President of the Neural Information Processing (NIPS) Foundation. He is the author of The Deep Learning Revolution (MIT Press) and other books.

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