Natural language processing (NLP) is one of the most important technologies of the information age. In traditional NLP, task-specific feature engineering and language-specific solutions were common. Recently, deep learning approaches have obtained very high performance across many different NLP tasks and multilingual solutions have been introduced. This course covers cutting-edge research in deep learning applied to NLP. Topics include word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component. A term project to implement, train, test, and visualize a custom neural network solution to a large scale NLP problem will be given.