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Deep learning

By: Goodfellow, Ian.
Contributor(s): Bengio, Yoshua | Courville, Aaron.
Material type: materialTypeLabelBookSeries: Adaptive computation and machine learning.Publisher: Cambridge, Mass. : MIT Press, ©2016Description: xxii, 775 p. : ill. ; 25 cm.ISBN: 9780262035613.Subject(s): Machine learning
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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Item type Current location Collection Call number Status Notes Date due Barcode Item holds Course reserves
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Computer Science and Engineering 006.31 GOO-D (Browse shelf) Not For Loan Vol.1 (2 volumes set) copy13
Reference Reference IIITD
Staff Office
Computer Science and Engineering 006.31 GOO-D (Browse shelf) Not For Loan Vol.2 (2 volumes set) copy14
Books Books IIITD
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Computer Science and Engineering REF 006.31 GOO-D (Browse shelf) Not For Loan 007352

Deep Learning UG/PG Winter

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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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