Computational Linear Algebra 1: Matrix Math, Accuracy, Memory, Speed, & Parallelization
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 Data Science (MSDS) 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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