# Computational Linear Algebra for Coders

Other,, Summer 2017 , Prof. Rachel Thomas

Updated On 02 Feb, 19

Other,, Summer 2017 , Prof. Rachel Thomas

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.

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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

Sam

Sep 12, 2018

Excellent course helped me understand topic that i couldn't while attendinfg my college.

Dembe

March 29, 2019

Great course. Thank you very much.