Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
By: Goodfellow, Ian [author.].
Contributor(s): Bengio, Yoshua [author.] | Courville, Aaron [author.].
Material type:
BookSeries: Adaptive computation and machine learning.Publisher: Cambridge, Massachusetts : The MIT Press, [2016]Copyright date: ©2016Description: xxii, 775 p. : ill. (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: 006.31
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.
| Item type | Current location | Call number | Copy number | Status | Date due |
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Indian Institute of Management Udaipur A3/1 | 006.31 (Browse shelf) | 1 | Available |
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| 006.3 Introduction to Artificial Intelligence | 006.31 Deep learning / | 006.312 Data mining and business analytics with R / | 006.312 Too big to ignore : | 006.312 Automated data collection with R : |
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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