Learning Linear Algebra
Motivation
For topics like:
- Computer Tomography / Medical Imaging technologies
- Computer Vision
- Machine Learning
- 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: sincXpressNo bullshit guide to linear algebra
author: Ivan SavovMatrix 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 …)