Deep learning Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Material type:
TextSeries: Adaptive computation and machine learningPublisher: Cambridge, Massachusetts : The MIT Press, [2016]Copyright date: ©2016Description: 775 p. illustrations (some color) ; 24 cmContent type: - text
- unmediated
- volume
- 9780262035613
- 006.31 22
- Q325.5 .G66 2016
| Item type | Current library | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
Books
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Liberty General Stacks | Reference | 006.31 (Browse shelf(Opens below)) | In transit from Liberty to Tan Tao University since 07/17/2024 | A-2022-0036 | |
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Books
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Liberty General Stacks | Reference | 006.31 (Browse shelf(Opens below)) | In transit from Liberty to Tan Tao University since 07/17/2024 | A-2022-0037 |
Browsing Tan Tao University shelves, Shelving location: General Stacks, Collection: Reference Close shelf browser (Hides shelf browser)
| 006.31 Reinforcement learning: an introduction | 006.31 Reinforcement learning: an introduction | 006.31 Deep learning | 006.31 Deep learning | 006.31 Introduction to deep learning | 006.31 Introduction to deep learning | 006.31 A practical guide to neural nets |
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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