Computational Linear Algebra for Coders

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.

Beginner 0(0 Ratings) 0 Students enrolled English
Created by Admin corner
Last updated Wed, 08-Jun-2022
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Course overview
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

Curriculum for this course
10 Lessons 16:32:23 Hours
Lecture
10 Lessons 16:32:23 Hours
  • Matrix Math, Accuracy, Memory, Speed, & Parallelization
    Preview 01:42:57
  • Topic Modelling with SVD & NMF
    01:40:44
  • Review, New Perspective on NMF, & Randomized SVD
    01:41:14
  • Randomized SVD & Robust PCA
    01:31:15
  • Robust PCA & LU Factorization
    01:40:58
  • Block Matrix Mult, Broadcasting, & Sparse Storage
    01:45:57
  • Compressed Sensing for CT Scans
    01:37:02
  • Numba, Polynomial Features, How to Implement Linear Regression
    01:35:48
  • PageRank with Eigen Decompositions
    01:39:58
  • QR Algorithm to find Eigenvalues, Implementing QR Decomposition
    01:36:30
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