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.
In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP. 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. Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age.Īpplications 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. For external enquiries, emergencies, or personal matters that you don't wish to put in a private Ed post, you can email us at What is this course about? You will find the course Ed on the course Canvas page or in the header link above. Contact: Students should ask all course-related questions in the Ed forum, where you will also find announcements.Office hours: Hybrid format with remote (over Zoom) or in person options.We are happy for anyone to use these resources, and we are happy to get acknowledgements. The lecture slides and assignments are updated online each year as the course progresses. Stanford students enroll normally in CS224N and others can also enroll in CS224N via Stanford online in the (northern hemisphere) Autumn to do the course in the Winter (high cost, limited enrollment, gives Stanford credit).
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Unfortunately, it is not possible to make these videos viewable by non-enrolled students. Lecture videos for enrolled students: are posted on Canvas (requires login) shortly after each lecture ends.
In-person lectures will start with the first lecture.
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