Deep learning /
| Auteur principal: | |
|---|---|
| Autres auteurs: | , |
| Format: | Livre |
| Langue: | English |
| Publié: |
Cambridge, Massachusetts :
The MIT Press,
c2016
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| Collection: | Adaptive computation and machine learning
|
| Sujets: | |
| Classic Catalogue: | View this record in Classic Catalogue |
Table des matières:
- 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.