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.