Introduction to NLP and Deep Learning, Language Modeling, History and Applications, Basic Text Processing, Simple Word Vector representations: word2vec, GloVe, Advanced word vector representations: language models, softmax, single layer networks, Neural Networks and back-propagation -- for named entity recognition; Gradient checks, overfitting, regularization, activation functions; Recurrent neural networks -- for language modeling and other tasks; GRUs and LSTMs, Recursive neural networks -- for parsing and other applications; Convolutional neural networks -- for sentence classification; Reinforcement Learning and applications, The future of Deep Learning for NLP: Dynamic Memory Networks.