Deep Learning

Introduction. Linear Algebra, Numerical Computation. Deep Feedforward Networks. Regularization for Deep Learning. Optimization for Training Deep Models. Convolutional Networks. Autoencoders and Representation Learning. Sequence Modeling: Recurrent and Recursive Nets. Generative Adversarial Networks. Deep Reinforcement Learning. Deep Learning applications.