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

By: Goodfellow, Ian [author.].
Contributor(s): Bengio, Yoshua [author.] | Courville, Aaron [author.].
Material type: TextTextSeries: Adaptive computation and machine learning.Publisher: Cambridge, Massachusetts : The MIT Press, [2016]Copyright date: ©2016Description: xxii, 775 pages : Rs. 1850.00 illustrations (some color) ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780262035613 (hardcover : alk. paper); 0262035618 (hardcover : alk. paper).Subject(s): Machine learningDDC classification: 006.3/1
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.
List(s) this item appears in: 2019-08-30
Item type Current location Call number Status Date due Barcode Item holds
Book Chennai Mathematical Institute
General Stacks 		006.31 GOO (Browse shelf) Available 10706
Total holds: 0

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.