Part I:
- Applied Math and Machine Learning Basics
- Linear Algebra
- Probability and Information Theory
- Numerical Computation
- Machine Learning Basics
Part II:
- Modern Practical Deep Networks
- Deep Feedforward Networks
- Regularization for Deep Learning
- Optimization for Training Deep Models
- Convolutional Networks
- Sequence Modeling: Recurrent and Recursive Nets
- Practical Methodology
- Applications
Part III:
- 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
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