Deep learning /
| 主要作者: | |
|---|---|
| 其他作者: | , |
| 格式: | 图书 |
| 语言: | English |
| 出版: |
Cambridge, Massachusetts :
The MIT Press,
c2016
|
| 丛编: | Adaptive computation and machine learning
|
| 主题: | |
| Classic Catalogue: | View this record in Classic Catalogue |
书本目录:
- 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.