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.
I think at the beginning of learning LA I would have benefited from a more broad introduction to the topic by explaining that it is the algebra of transformations, generally linear transformations, and also the art of quantifying those transformations in meaningful ways.
I would have benefited from some more handwaving in this regard (matrix multiplication, eigenvectors and eigenvalues) and less on the mechanics of the operations, before starting on the basic technicalities. But a “lesson” on these topics on day 0 is too soon
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.
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/
You missed How to Think Like a Computer Scientist.
Many places on the web. Runestone is probably the most useful like but I’ll leave my favorite classic one below.
http://www.openbookproject.net/thinkcs/python/english3e/
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
And eigenvectors in the first lesson!
I think at the beginning of learning LA I would have benefited from a more broad introduction to the topic by explaining that it is the algebra of transformations, generally linear transformations, and also the art of quantifying those transformations in meaningful ways.
I would have benefited from some more handwaving in this regard (matrix multiplication, eigenvectors and eigenvalues) and less on the mechanics of the operations, before starting on the basic technicalities. But a “lesson” on these topics on day 0 is too soon
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.
what's the deal with the loop example? am i supposed to understand what this represents before going through the material?
Linear Algebra is dope, as in when we got to apply some mid-level linear to a real business problem and it worked i got high
What was the business problem, broadly? How did you apply linear algebra to it?
I got my hands on a stanford Math 55 textbook and tried to do the exercises in numpy.