Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Textual Documents
Material type: TextLanguage: English Series: Adaptive computation and machine learningPublisher: Cambridge, Massachusetts : The MIT Press, [2016]Copyright date: ?2016Description: xxii, 775 pages : illustrations (some color) ; 24 cmContent type:- text
- unmediated
- volume
- 0262035618 (hardcover : alk. paper)
- 9780262035613 (hardcover : alk. paper)
- 23 006.31
- Q325.5 .G66 2016
Item type | Current library | Home library | Collection | Call number | Copy number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|---|
Book | Dept. of Computer Science | Dept. of Computer Science | 006.31 (Browse shelf(Opens below)) | 1 | Available | DCS4527 | |||
Book | Dept. of Futures Studies General Stacks | Dept. of Futures Studies | Non-fiction | 006.31 GOO (Browse shelf(Opens below)) | Checked out to Rahul B (DFSFM06) | 09/10/2024 | DFS4505 |
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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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