NUST Institutions Library Catalogue

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 learningPublisher: Cambridge, Massachusetts : The MIT Press, [2016]Copyright date: ©2016Description: xxii, 775 pages : illustrations (some color) ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780262035613 (hardcover : alk. paper); 0262035618 (hardcover : alk. paper)Subject(s): Machine learningDDC classification: 006.31 GOO LOC classification: Q325.5 | .G66 2016Online resources: Click here to access online
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 Home library Collection Shelving location Call number Status Date due Barcode Item holds
Book Book Central Library (CL)
Central Library (CL)
280 SCB (Browse shelf) Available 13723
Book Book Central Library (CL)
Central Library (CL)
NFIC First Floor - Artificial Intelligence/Data Mining 006.31 GOO (Browse shelf) Checked out 04/26/2026 SEECS013723
Book Book Central Library (CL)
Central Library (CL)
NFIC First Floor - Artificial Intelligence/Data Mining 006.31 GOO (Browse shelf) Available SEECS013724
Book Book Central Library (CL)
Central Library (CL)
NFIC First Floor - Artificial Intelligence/Data Mining 006.31 GOO (Browse shelf) Checked out 10/09/2025 SEECS013725
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.

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