Deep learning / Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Series: Adaptive computation and machine learningPublication details: Cambridge, Massachusetts : The MIT Press, c2016Description: xxii, 775 pages : illustrations (some color) ; 24 cmISBN:- 9780262035613 (hardcover : alk. paper)
- 0262035618 (hardcover : alk. paper)
- 006.31 23
- Q325.5 .G66 2016
Item type | Current library | Home library | Call number | Copy number | Status | Date due | Barcode | Item holds |
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Ayesha Abed Library General Stacks | Ayesha Abed Library General Stacks | 006.31 GOO (Browse shelf(Opens below)) | 1 | Checked out | 07/07/2024 | 3010032607 | |
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Ayesha Abed Library General Stacks | Ayesha Abed Library General Stacks | 006.31 GOO (Browse shelf(Opens below)) | 2 | Checked out | 08/07/2024 | 3010032608 |
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006.31 GER Hands-on machine learning with Scikit-Learn and TensorFlow : | 006.31 GER Hands-on machine learning with Scikit-Learn and TensorFlow : | 006.31 GOL Genetic algorithms in search, optimization, and machine learning / | 006.31 GOO Deep learning / | 006.31 GOO Deep learning / | 006.31 HAS The elements of statistical learning : | 006.31 HAS The elements of statistical 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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