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

By: Contributor(s): Material type: TextTextLanguage: English Series: Adaptive computation and machine learningPublication details: Cambridge, MA : MIT Press, 2017.Description: xxii, 775 pages : illustrations (some color) ; 24 cmISBN:
  • 9780262035613
  • 0262035618
Subject(s): DDC classification:
  • 006.31 23 GOO
Contents:
Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- 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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Item type Current library Collection Call number Status Barcode
General Books General Books CUTN Central Library Generalia Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 46847
General Books General Books CUTN Central Library Generalia Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 36356
General Books General Books CUTN Central Library Generalia Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 36357
General Books General Books CUTN Central Library Generalia Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 34304

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

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

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