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Deep learning
By: Goodfellow, Ian.
Contributor(s): Bengio, Yoshua | Courville, Aaron.
Material type:
BookSeries: 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.
| Item type | Current location | Collection | Call number | Status | Notes | Date due | Barcode | Item holds | Course reserves |
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Reference
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IIITD Staff Office | Computer Science and Engineering | 006.31 GOO-D (Browse shelf) | Not For Loan | Vol.1 (2 volumes set) | copy13 | |||
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Reference
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IIITD Staff Office | Computer Science and Engineering | 006.31 GOO-D (Browse shelf) | Not For Loan | Vol.2 (2 volumes set) | copy14 | |||
Books
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IIITD Reference | Computer Science and Engineering | REF 006.31 GOO-D (Browse shelf) | Not For Loan | 007352 |
Total holds: 1
Browsing IIITD Shelves , Shelving location: Staff Office , Collection code: Computer Science and Engineering Close shelf browser
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| Kindle ebook reader | Kindle ebook reader | Kindle ebook reader | 006.31 GOO-D Deep learning | 006.31 GOO-D Deep learning |
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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