Computational Linear Algebra for Coders
Other,, Summer 2017 , Prof. Rachel Thomas
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Updated On 02 Feb, 19
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.
4.1 ( 11 )
Course materials available here: https://github.com/fastai/numerical-linear-algebra
Answer questions from the previous class about randomized projections, history of Gaussian elimination, and instability of LU without pivots.
Cover block matrix multiplication, numpy broadcasting, and sparse matrix storage formats.
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/analytics
Ask questions about the course on our fast.ai forums: http://forums.fast.ai/c/lin-alg
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
March 29, 2019
Great course. Thank you very much.