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

By: Contributor(s): Material type: TextTextLanguage: English Series: Adaptive computation and machine learningPublisher: Cambridge, Massachusetts : The MIT Press, [2016]Copyright date: ?2016Description: xxii, 775 pages : illustrations (some color) ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 0262035618 (hardcover : alk. paper)
  • 9780262035613 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 23 006.31
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.
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Holdings
Item type Current library Home library Collection Call number Copy number Status Date due Barcode
Book Book Dept. of Computer Science Dept. of Computer Science 006.31 (Browse shelf(Opens below)) 1 Checked out to Ajmal Samadi (DCSRSFT08) 21/02/2025 DCS4527
Book Book Dept. of Futures Studies General Stacks Dept. of Futures Studies Non-fiction 006.31 GOO (Browse shelf(Opens below)) Checked out to Rahul B (DFSFM06) 01/03/2025 DFS4505

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.

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