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Deep learning approaches have obtained very high performance across many different natural language processing tasks. In this class, students will learn to understand, implement, train, debug, visualize and potentially invent their own neural network models for a variety of language understanding tasks. The course provides a deep excursion from early models to cutting-edge research. Applications will range across a broad spectrum: from simple tasks like part of speech tagging, over sentiment analysis to question answering and machine translation. The final project will involve implementing a complex neural network model and applying it to a large scale NLP problem. Prerequisites: programming abilities (python), linear algebra, Math 21 or equivalent, machine learning background ( CS 229 or similar) Recommended: machine learning ( CS 229, CS 228), CS 224N, EE364a (convex optimization), CS 231N