Deep learning /
| Автор: | |
|---|---|
| Інші автори: | , |
| Формат: | Книга |
| Мова: | English |
| Опубліковано: |
Cambridge, Massachusetts :
The MIT Press,
c2016
|
| Серія: | Adaptive computation and machine learning
|
| Предмети: | |
| Classic Catalogue: | View this record in Classic Catalogue |
Зміст:
- 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.