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Matrix multiplication introduced before vector addition... the "Linear Algebra Done Right" in me is screaming inside. That being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra
Matrix multiplication introduced before vector addition... the "Linear Algebra Done Right" in me is screaming inside. That being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra
One of the challenges of learning Linear Algebra is where to start. Most textbooks start with vector arithmetic, which make senses if you are working with paper and pencil, but they take a long time to get to something useful.
With a computational approach, we have the option to proceed top-down -- that is, we can start with libraries that implement the core algorithms of linear algebra, and wait until later to see how they work. With this approach we can can get to the good stuff faster.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.
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. 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/
- Think Python
- Think Data Structures
- Think Java
- Think Perl6 (!)
- Modeling and Simulation in Python
- Probably Overthinking It
And more [1]. He's a prolific writer, and very generous for offering many of them for free. I read several of them online or through O'Reilly, and bought printed copies just to appreciate his work. Really enjoyed Think DSP, Think Complexity, Think Bayes, etc.
[1] https://www.amazon.com/stores/Allen-Downey/author/B001O8NBPS
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. 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/
Matrix multiplication introduced before vector addition... the "Linear Algebra Done Right" in me is screaming inside. That being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra
Matrix multiplication introduced before vector addition... the "Linear Algebra Done Right" in me is screaming inside. That being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra
What would you suggest as a complimentary resource to this?
Matrix multiplication introduced before vector addition... the "Linear Algebra Done Right" in me is screaming inside. That being said, it is definitely cool to have a Jupyter-notebook based set of examples of practical linear algebra
Vector addition is just matrix multiplication in a homogeneous coordinate system, what's the problem?
Earlier quoted context omitted.
What would you suggest as a complimentary resource to this?
I think GP is both referring to and suggesting: https://linear.axler.net/
Strang can be great as a first book. He focuses more on what rather than why, so if one wants to delve deeper, it needs to be supplemented by a few other books.