Amazon cover image
Image from Amazon.com

Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

By: Contributor(s): Series: Adaptive computation and machine learningPublication details: Cambridge, Massachusetts : The MIT Press, c2016Description: xxii, 775 pages : illustrations (some color) ; 24 cmISBN:
  • 9780262035613 (hardcover : alk. paper)
  • 0262035618 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 006.31 23
LOC classification:
  • Q325.5 .G66 2016
Contents:
Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 5.0 (1 votes)
Holdings
Item type Current library Home library Call number Copy number Status Date due Barcode Item holds
Book Book Ayesha Abed Library General Stacks Ayesha Abed Library General Stacks 006.31 GOO (Browse shelf(Opens below)) 1 Checked out 07/07/2024 3010032607
Book Book Ayesha Abed Library General Stacks Ayesha Abed Library General Stacks 006.31 GOO (Browse shelf(Opens below)) 2 Checked out 08/07/2024 3010032608
Total holds: 0

Includes bibliographical references (pages [711]-766) and index.

Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.

AAL

There are no comments on this title.

to post a comment.
Share