Course

Historical Trends in Deep Learning, Linear Algebra, Probability and Information theory, Numerical Computation, Machine learning basics

Deep Networks: Deep feedforward networks, Regularization for deep learning, Optimization for training deep models, Convolutional Networks, Sequence modelling: Recurrent and recursive nets, Practical methodology, Applications

Deep Learning Research: Linear factor models, autoencoders, Representation learning, Structured probabilistic models, Monte-Carlo models,

Intro to NLP and Deep Learning, Simple Word Vector representations

Introduction to Tensorflow

Convolutional neural networks- Dynamic Memory Networks.

1. Ian Goodfellow, YoshuaBengio, Aaron Courville, Deep Learning, MIT Press, 2016
2. Yoav Goldberg, A Primer on Neural Network Models for Natural Language Processing, 2015
3. Jurafsky, James H. Martin, Speech and Language Processing, 3rd edition, 2017