书籍目录
- Introduction
- Linear Algebra
- Probability and Information Theory
- Numerical Computation
- Machine Learning Basics
- Deep Feedforward Networks
- Regularization for Deep Learning
- Optimization for Training Deep Models
- Convolutional Networks
- Sequence Modeling: Recurrent and Recursive Nets
- Practical Methodology
- Applications
- Linear Factor Models
- Representation Learning
- Structured Probabilistic Models for Deep Learning
- Confronting the Partition Function
- Approximate Inference
- Deep Generative Models
书籍简介
- Introduction
- Linear Algebra
- Probability and Information Theory
- Numerical Computation
- Machine Learning Basics
- Deep Feedforward Networks
- Regularization for Deep Learning
- Optimization for Training Deep Models
- Convolutional Networks
- Sequence Modeling: Recurrent and Recursive Nets
- Practical Methodology
- Applications
- Linear Factor Models
- Representation Learning
- Structured Probabilistic Models for Deep Learning
- Confronting the Partition Function
- Approximate Inference
- Deep Generative Models
本书pdf是文字版 。