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

By: Contributor(s): Material type: TextLanguage: 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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Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
General Books CUTN Central Library Generalia Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 46847
General Books CUTN Central Library Generalia Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 36356
General Books CUTN Central Library Generalia Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 36357
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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