Allen Downey (author of the above) has a number of books on computer science-y things. You can buy hardcopies but I think all of them are also just freely available.
Beyond regression, I’d like to see chapters on statistical topics like PCA, CCA. This textbook format which interleaves code and prose is the perfect way to show how scikitlearn’s decomposition.cca and decomposition.pca are implemented, e.g. the SVD matrix decomposition, etc.
I saw a linear algebra “textbook” on Twitter in maybe 2022? It was black background and bright text with a good amount of graphs like someone’s incredibly long blog post. I’ve tried a few times to find it since but haven’t had any luck.
This looks a bit more involved but lovely I think I’ll try it. I read Think Bayes and thought it was great.
Downey's "Think X" series is consistently the on-ramp for people who learned to code before they learned the math, and honestly at this point everything is linear algebra
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[ 2.8 ms ] story [ 39.1 ms ] threadThat being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra
It's title is:
"Linear Algebra, Multivariable Calculus, and Modern Applications, Math 51 course text prepared by the Stanford University Math Department"
Here's a few:
Think Complexity
https://github.com/AllenDowney/ThinkComplexity2
Think DSP
https://github.com/AllenDowney/ThinkDSP
Think Stats
https://github.com/AllenDowney/ThinkStats/
Think Bayes
https://github.com/AllenDowney/ThinkBayes2/
https://www.t3x.org/klong/
Quick ref:
https://www.t3x.org/klong/klong-qref.txt.html
Intro:
https://www.t3x.org/klong/klong-intro.txt.html
Klong for K users:
https://www.t3x.org/klong/klong-vs-k.txt.html
This looks a bit more involved but lovely I think I’ll try it. I read Think Bayes and thought it was great.