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

By: Goodfellow, Ian | [author.]Contributor(s): Bengio, Yoshua | Courville, Aaron | [author.] | [author.]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): Machine learningDDC classification: 006.31
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 Date due Barcode
General Books General Books CUTN Central Library

This is a searchable open catalogue of all library of the Central University of Tamil Nadu.

Generalia 		Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 36356
General Books General Books CUTN Central Library

This is a searchable open catalogue of all library of the Central University of Tamil Nadu.

Generalia 		Non-fiction 006.31 GOO (Browse shelf(Opens below)) Available 36357
General Books General Books CUTN Central Library

This is a searchable open catalogue of all library of the Central University of Tamil Nadu.

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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