Stanford CS224N NLP with Deep Learning - bancron/stanford-cs224n GitHub Wiki

Stanford CS224N, also listed as Ling284, is a graduate-level Stanford course on NLP with Deep Learning. It is taught approximately once every two years.

The lectures and course material are freely available to the public.

Lectures: link

Slides, notes, and homework: link

In the words of the instructor, this course’s topic:

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertising, emails, customer service, language translation, virtual agents, medical reports, politics, etc. In the last decade, deep learning (or neural network) approaches have obtained very high performance across many different NLP tasks, using single end-to-end neural models that do not require traditional, task-specific feature engineering. In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework.