Asia Pacific University Library catalogue


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

By: Goodfellow, IanContributor(s): Bengio, Yoshua | Courville, AaronMaterial type: TextTextSeries: Adaptive computation and machine learningPublication details: New York : Springer, c2016Description: xxii, 775 p. : ill. (some col. ) ; 24 cmISBN: 9780262035613 (hardcover : alk. paper); 0262035618 (hardcover : alk. paper)Subject(s): Machine learningDDC classification: 006.3/1 LOC classification: Q325.5 | .G66 2016
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 library Collection Call number Copy number Status Date due Barcode
General Circulation General Circulation APU Library
Open Shelf 		Book Q325.5 .G66 2016 c.1 (Browse shelf (Opens below)) 1 Available 00012808

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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