[PNWChris]

They’re much narrower in scope than a full curriculum (or even a course, for that matter), but Better Explained [0] has some very good overviews of math topics. They are a useful supplement I’ve come across repeatedly while searching topics I found challenging over the years.

On the topic of open source learning, I take every chance I can to heartily recommend fast.ai’s course [1]. It’s a good intro to Deep Learning that leaves you informed enough to build things, and equips you to ask follow-on questions and dive deeper when/where you need to.

[0]: https://betterexplained.com/

[1]: https://course.fast.ai/

[jph00]

Perhaps more relevant for this topic is the computational linear algebra course from fast.ai:

https://www.fast.ai/2017/07/17/num-lin-alg/

It has a lot more detail on stuff like floating point storage, memory layout, sparse matrices, iterative methods, etc than most linear algebra courses, but doesn't go much in to proofs, geometric interpretations, and other stuff that's less needed for algorithm design and implementation.

(Disclaimer, I'm from fast.ai.)