Learning Linear Algebra

Motivation

For topics like:

  1. Computer Tomography / Medical Imaging technologies
  2. Computer Vision
  3. Machine Learning
  4. Data analysis / Statistics

I have found that more solid skills in numerical math and linear algebra would be needed to understand books / publications rather than to follow a cookbook approach.

While learning a bit more about Linear algebra beyond what was required at the workplace, I have setup some Jupyter notebooks. From experience I found learning from textual sources (books, articles) more efficient than the numerous videos available on YouTube.

The Jupyter notebooks are available on GitHub:

https://github.com/michaelbiester/LinearAlg/tree/master

The Readme file provides an overview of the notebooks and their content. For the more mature notebooks a PDF versions is provided for better readability.


Resources

  • Calculus, Paul Dawkins (available as PDF document)
  • MATHEMATICS FOR MACHINE LEARNING , Deisenroth et. al. (available as PDF)
  • Linear Algebra : Theory, Intuition, Code author: Mike X Cohen, publisher: sincXpress
  • No bullshit guide to linear algebra author: Ivan Savov
  • Matrix Methods for Computational Modeling and Data Analytics author: Mark Embree, Virginia Tech (a PDF document can be found on the web; just search for the title …)