# 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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Course materials available here: https://github.com/fastai/numerical-linear-algebra

Compressed sensing is critical to allowing CT scans with lower radiation-- the image can be reconstructed with less data. Here we will learn the technique and apply it to CT images.

Numpy Broadcasting

Sparse matrices

CT Scans and Compressed Sensing

L1 and L2 regression

These topics are reviewed in the Lesson 8 Video.

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.