An Introduction to Neural Networks
eBook - ePub

An Introduction to Neural Networks

  1. 234 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

An Introduction to Neural Networks

About this book

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

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Yes, you can access An Introduction to Neural Networks by Kevin Gurney in PDF and/or ePUB format, as well as other popular books in Computer Science & Information Technology. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover Page
  2. Halftitle Page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Preface
  7. Chapter One: Neural networks - an overview
  8. Chapter Two: Real and artificial neurons
  9. Chapter Three: TLUs, linear separability and vectors
  10. Chapter Four: Training TLUs: the perceptron rule
  11. Chapter Five: The delta rule
  12. Chapter Six: Multilayer nets and backpropagation
  13. Chapter Seven: Associative memories: the Hopfield net
  14. Chapter Eight: Self-organization
  15. Chapter Nine: Adaptive resonance theory: ART
  16. Chapter Ten: Nodes, nets and algorithms: further alternatives
  17. Chapter Eleven: Taxonomies, contexts and hierarchies
  18. Appendix A
  19. References
  20. Index