[dawnofdusk]

>My goal is to develop a practical, working understanding I can apply directly.

Apply directly... to what? IMO it is weird to learn theory (like linear algebra) expressly for practical reasons: surely one could just pick up a book on those practical applications and learn the theory along the way? And if in this process, you end up really needing the theory then certainly there is no substitute for learning the theory no matter how dense it is.

For example, linear algebra is very important to learning quantum mechanics. But if someone wanted to learn linear algebra for this reason they should read quantum mechanics textbooks, not linear algebra textbooks.

[xwowsersx]

You're totally right. I left out the important context. I'm learning linear algebra mainly for applied use in ML/AI. I don't want to skip the theory entirely, but I've found that approaching it from the perspective of how it's actually used in models (embeddings, transformations, optimization, etc.) helps me with motivation and retaining.

So I'm looking for resources that bridge the gap, not purely computational "cookbook" type resources but also not proof-heavy textbooks. Ideally something that builds intuition for the structures and operations that show up all over ML.

[blackbear_]

Strang's Linear algebra and learning from data is extremely practical and focused on ML

https://math.mit.edu/~gs/learningfromdata/

Although if your goal is to learn ML you should probably focus on that first and foremost, then after a while you will see which concepts from linear algebra keep appearing (for example, singular value decomposition, positive definite matrices, etc) and work your way back from there