There are also coursera specializations from Andrew Ng at https://deeplearning.ai.

Ng's course is bottom up: Start with the basic math, expand upon it, until you arrive at ML and neural nets.

Fastai is top down: learn to use practical ML with abstractions, and then dig deeper and explain as needed.

I preferred fastai's approach, even though I enjoyed both. Ng's could be a bit too low level and fundamental for what I wanted to learn.

This is a valuable take. Fastai was very frustrating for me because I wanted to understand the internals. I ended up not finishing it, so take my opinion with a grain of salt.

I much prefer Andrew Ng's courses as well.

I tried Fast AI, but it seems to be trying too hard to take out the math, which oddly for me (as a STEM grad) makes it much more difficult to understand.

Had to stop when I saw him using Excel spreadsheets to explain convolution.

The Excel spreadsheets is where it all clicked for me. It's not done with some magical library but with simple math being performed on some weights.