I'll recommend Linear Algebra: A Modern Introduction by David Poole (which I picked up rather randomly in a library clearance sale for $2). It tackles most subjects from both algebraic and geometric perspectives, so from the visual aspect it might fit. What's particularly useful about it relative to HN is it leans into computational applications pretty heavily.
For example, if some particular method is computationally efficient relative to others, the text makes a note of it, and has lots of computational examples. Most of the examples could be set up fairly straightforwardly with something like a Python notebook and Numpy for matrices. It also covers things like computational errors wrt floating-point operations when doing vector and matrix calculations, efficient algorithms for approximating eigenvalues of a matrix, etc.
And!, the full text is available on archive.org with a free account:
For example, if some particular method is computationally efficient relative to others, the text makes a note of it, and has lots of computational examples. Most of the examples could be set up fairly straightforwardly with something like a Python notebook and Numpy for matrices. It also covers things like computational errors wrt floating-point operations when doing vector and matrix calculations, efficient algorithms for approximating eigenvalues of a matrix, etc.
And!, the full text is available on archive.org with a free account:
https://archive.org/details/linearalgebramod0000pool