SignUpComputational Linear Algebra for Coders
Other,, Summer 2017 , Prof. Rachel Thomas
Updated On 02 Feb, 19
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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.
4.1 ( 11 )
Course materials available here: https://github.com/fastai/numerical-linear-algebra
We review randomized SVD & robust PCA (for background removal on a surveillance video), and introduce Gaussian Elimination & LU factorization.
Topics covered here reviewed in next video.
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.
