Stanford's 2019 Deep Learning NLP course is finished, the video (with subtitles) has been released | Attached PPT, reference materials, excellent projects

Latest update time：2019-03-29

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Qian Ming from Aofei Temple
Quantum Bit Report | Public Account QbitAI

Stanford 2019 Deep Learning NLP course resources are out!

Most of the course videos (with subtitles) are online, all course PPTs are released, and the excellent projects in the course are also public.

Compared with the previous version of the Stanford NLP course (2017), this course has been greatly updated, adding content such as Transformer and pre-trained representations that have been very popular in the past two years.

If you are interested in NLP, don't miss it~

What did the course cover?

The course is taught by Chris Manning, professor of computer science and linguistics at Stanford University, and his student Abigail See.

Christopher Manning is the director of the Artificial Intelligence Laboratory at Stanford University and a Fellow of ACM and AAAI.

The course has a total of 20 lectures, covering topics such as word vectors, back propagation, neural networks, RNNs and language models, gradient disappearance, machine translation, natural language generation , etc. The last section introduces the future of NLP.

This year's course is more compact than previous years. The introduction to NLP has been cancelled. The first class goes straight to the point and teaches word vectors.

What’s more, the additional reading for the second lecture includes the paper On the Dimensionality of Word Embedding from NeurIPS 2018.

It is important to note that this year's course uses PyTorch for the first time, replacing the previous TensorFlow.

Course viewing guide

Course website, with PPT and reference materials:
http://web.stanford.edu/class/cs224n/index.html#schedule

Official course video website:
http://onlinehub.stanford.edu/cs224

B station video link (with subtitles):
https://www.bilibili.com/video/av46216519

Course Outstanding Project Website:
http://web.stanford.edu/class/cs224n/project.html

Finally, a reminder~

This course is not for beginners, so you need to master the following knowledge before you can understand it:

Python: including using NumPy and PyTorch
College Calculus, Linear Algebra: Calculus of Multivariate Functions, Matrices, Vectors
Basic probability and statistics: Gaussian distribution, mean, standard deviation
Basics of machine learning: I recommend reading the first 5 chapters of Hal Daumé's machine learning course

-over-

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