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Computational Linear Algebra for Coders

Other,, Summer 2017 , Prof. Rachel Thomas

Updated On 02 Feb, 19

Overview

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.

Includes

Lecture 3: Computational Linear Algebra 3: Review, New Perspective on NMF, & Randomized SVD

4.1 ( 11 )


Lecture Details

Course materials available here: https://github.com/fastai/numerical-linear-algebra
We review the topics from videos 1 and 2. We look at a new example of topic modeling (using NMF and SVD) on a collection of 27 classic British novels. We cover how randomized SVD can be used to calculate a truncated SVD.

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

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Comments
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Sam

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

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Dembe

Great course. Thank you very much.

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