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.

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.