Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Material type:
TextLanguage: English Series: Adaptive computation and machine learningPublication details: Cambridge, MA : MIT Press, 2017. Description: xxii, 775 pages : illustrations (some color) ; 24 cmISBN: 9780262035613 ; 0262035618 Subject(s): Machine learningDDC classification: 006.31 | Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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General Books
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CUTN Central Library
This is a searchable open catalogue of all library of the Central University of Tamil Nadu. |
Non-fiction | 006.31 GOO (Browse shelf(Opens below)) | Available | 36356 | |
|
General Books
|
CUTN Central Library
This is a searchable open catalogue of all library of the Central University of Tamil Nadu. |
Non-fiction | 006.31 GOO (Browse shelf(Opens below)) | Available | 36357 | |
|
General Books
|
CUTN Central Library
This is a searchable open catalogue of all library of the Central University of Tamil Nadu. |
Non-fiction | 006.31 GOO (Browse shelf(Opens below)) | Available | 34304 |
Browsing CUTN Central Library shelves, Shelving location: Generalia, Collection: Non-fiction Close shelf browser (Hides shelf browser)
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| 006.31 GOL Genetic algorithms in search, optimization, and machine learning | 006.31 GOO Deep learning / | 006.31 GOO Deep learning / | 006.31 GOO Deep learning / | 006.31 JOS Adversarial machine learning / | 006.31 LIU Computational trust models and machine learning / | 006.31 MIC Applied deep learning : |
Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models.
Includes bibliographical references (pages 711-766) and index.
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