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Neural Networks and Learning Machines Simon O. Haykin.

By: Material type: TextTextPublisher number: 9780131471399 | Prentice HallSeries: 3rd editioPublication details: New Delhi Prentice Hall, 2009Edition: 3rd edDescription: xxx; 906 p. 24 cmISBN:
  • 0131471392 (hardcover)
  • 9780131471399 (hardcover)
DDC classification:
  • 3rd. 006.3105195 HAY-N
LOC classification:
  • 006.3105195 HAY-N
Online resources: Summary: For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. �� Neural Networks and Learning Machines, Third Edition is 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. This is ideal for professional engineers and research scientists. �� Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ �� Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independentl
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Holdings
Item type Current library Collection Call number Status Notes Barcode
Book Book Air University Central Library Islamabad Computer Science Computer Engineering 006.3105195 HAY-N (Browse shelf(Opens below)) Available Program Relevancy: BEMTS; BEME Course Relevancy: Machine design P9241
Browsing Air University Central Library Islamabad shelves, Shelving location: Computer Science , Collection: Computer Engineering Close shelf browser (Hides shelf browser)
005.133 CHO Deep learning with Python 005.8 VAC-N Network and System Security 006.3105195 HAY-N Neural Networks and Learning Machines 006.32 SIV-I Introduction to Neural Networks Using Matlab 6.0

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. �� Neural Networks and Learning Machines, Third Edition is 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. This is ideal for professional engineers and research scientists. �� Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ �� Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independentl

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