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Deep learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Material type:
TextSeries: 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
- 9780262035613
- 006.31 22 GOO
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 library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Books
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Learning Resource Centre | 006.31 GOO (Browse shelf(Opens below)) | Checked out | 16/08/2022 | 14754 | |
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Books
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Learning Resource Centre | 006.31 GOO (Browse shelf(Opens below)) | Available | 14755 |
Total holds: 0
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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, Keras 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 GOP Applied machine learning / | 006.31 GOP Applied machine 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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