Computational Linear Algebra for Coders
Other,, Summer 2017 , Prof. Rachel Thomas
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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
SVD is intimately connected to the eigen decomposition, so we will now learn how to calculate eigenvalues for a large matrix. We will use DBpedia, a large dataset of Wikipedia links, and the principal eigenvector gives the relative importance of different Wikipedia pages (this is the basic idea of Googles PageRank algorithm)
- Full vs Reduced Factorizations
- Matrix Inversion is Unstable
- DBpedia Dataset
- Power Method
This material is reviewed in the Lesson 10 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
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
March 29, 2019
Great course. Thank you very much.