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1 - 2 of 2 results for: CS 224N: Natural Language Processing with Deep Learning

CS 224D: Deep Learning for Natural Language Processing

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. We will introduce a common programming framework for deep learning for the problem sets.Prerequisites: programming abilities (python), linear algebra, Math 21 or equivalent, machine learning background ( CS 229 or similar) Recommended: CS 224N, EE364a (convex optimization), CS 231N
Last offered: Spring 2016

CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284)

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem. Prerequisites: calculus and linear algebra; CS124 or CS121/221.
Terms: Win | Units: 3-4 | Repeatable for credit
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