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Deep learning /
| Main Authors: | , , |
|---|---|
| Format: | Printed Book |
| Language: | English |
| Published: |
Cambridge, Massachusetts :
The MIT Press,
[2016]
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| Series: | Adaptive computation and machine learning
|
| Subjects: |
Table of 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.