Deep Learning for Natural Language Processing


Stanford School of Engineering



Deep learning approaches have obtained very high performance across many different natural language processing tasks. This course provides a deep excursion from early models to cutting-edge research to help you implement, train, debug, visualize and potentially invent your own neural network models for a variety of language understanding tasks.


Programming abilities (python), linear algebra, Math21 or equivalent, machine learning background (CS229 or similar).

CS224N, EE364A, or CS231N are recommended.

Topics include

  • Common programming frameworks
  • Complex neural network models
  • Large scale NLP problems
  • Machine translation
  • Sentiment analysis
  • Speech tagging