Other Course , Summer 2017 , Prof. Rachel Thomas

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Other Course , Summer 2017 , Prof. Rachel Thomas

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
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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- 1.Computational Linear Algebra 1: Matrix Math, Accuracy, Memory, Speed, & Parallelization
- 2.Computational Linear Algebra 2: Topic Modelling with SVD & NMF
- 3.Computational Linear Algebra 3: Review, New Perspective on NMF, & Randomized SVD
- 4.Computational Linear Algebra 4: Randomized SVD & Robust PCA
- 5.Computational Linear Algebra 5: Robust PCA & LU Factorization
- 6.Computational Linear Algebra 6: Block Matrix Mult, Broadcasting, & Sparse Storage
- 7.Computational Linear Algebra 7: Compressed Sensing for CT Scans
- 8.Computational Linear Algebra 8: Numba, Polynomial Features, How to Implement Linear Regression
- 9.Computational Linear Algebra 9: PageRank with Eigen Decompositions
- 10.Computational Linear Algebra 10: QR Algorithm to find Eigenvalues, Implementing QR Decomposition

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