This makes for an exciting time to learn NLP. NLP is still very much a field in flux, with best practices changing and new standards not yet settled on. There have been many major advances in NLP in the last year, and new state-of-the-art results are being achieved every month. Note that videos vary in length between 20-90 minutes. Most of the topics can stand alone, so no need to go through the course in order if you are only interested in particular topics (although I hope everyone will watch the videos on bias and disinformation, as these are important topics for everyone interested in machine learning). Risks raised by new language models such as GPT-2 How NLP advances are heightening risks of disinformation.A special guest lecture by Nikhil Garg on how word embeddings encode stereotypes (and how this has changed over the last 100 years).Issues of bias and some steps towards addressing them.Text generation algorithms (including the implementation of a new paper from the Allen Institute).Tips on working with languages other than English.Some highlights of the course that I’m particularly excited about: In previous years, Jeremy taught the machine learning course and I’ve taught a computational linear algebra elective as part of the program. The USF MSDS has been around for 7 years (over 330 students have graduated and gone on to jobs as data scientists during this time!) and is now housed at the Data Institute in downtown SF. This course was originally taught in the University of San Francisco MS in Data Science program during May-June 2019.
#NLP DEEP LEARNING CODE#
You can find all code for the notebooks available on GitHub and all the videos of the lectures are in this playlist. All videos for the course are on YouTube and all code is on GitHubĪll the code is in Python in Jupyter Notebooks, using PyTorch and the fastai library.
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