# 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

We use a dataset of messages posted on discussion forums to identify topics. A term-document matrix represents the frequency of the vocabulary in the documents. We factor it using Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF).

We use PyTorch as a GPU-accelerated alternative to Numpy to speed things up, and we cover Stochastic Gradient Descent, a very useful, general purpose optimization algorithm.

This video is fast-paced, so be sure to watch Lesson 3 for a review and Q&A of the topics covered here.

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

Topics covered:

- Singular Value Decomposition (SVD)

- Non-negative Matrix Factorization (NMF)

- Stochastic Gradient Descent (SGD)

- Intro to PyTorch

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.