Computational Linear Algebra for Coders

Other Course , Summer 2017 , Prof. Rachel Thomas

697 students enrolled

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.

Lecture 1: Computational Linear Algebra 1: Matrix Math, Accuracy, Memory, Speed, & Parallelization

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        Lecture Details

        Course materials available here: https://github.com/fastai/numerical-linear-algebra A high level overview of some foundational concepts in numerical linear algebra: - Matrix and Tensor Products - Matrix Decompositions - Accuracy - Memory use - Speed - Parallelization & Vectorization 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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