Numerical linear algebra, sometimes called applied linear algebra, is the study of how matrix operations can be used to create computer algorithms which efficiently and accurately provide approximate answers to questions in continuous mathematics. It is a subfield of numerical analysis, and a type of linear algebra.
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-l...
Course overview blog post: http://www.fast.ai/2017/07/17/num-lin...
Taught in the University of San Francisco MS in Analytics (MSAN) graduate program: https://www.usfca.edu/arts-sciences/g...
Ask questions about the course on our fast.ai forums: http://forums.fast.ai/c/lin-alg
This course is presented by Rachel Thomas