Published on March 2, 2018 by

This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy? The course is taught in Python with Jupyter Notebooks, using libraries such as scikit-learn and numpy for most lessons, as well as numba and pytorch in a few lessons.

Course materials are available on github:
https://github.com/fastai/numerical-linear-algebra

Course overview blog post:
http://www.fast.ai/2017/07/17/num-lin-alg/

Taught in the University of San Francisco MS in Analytics (MSAN) graduate program:
https://www.usfca.edu/arts-sciences/graduate-programs/data-science

Ask questions about the course on our fast.ai forums: http://forums.fast.ai/c/lin-alg

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