Does FAST.AI get into the grit of actually doing or is it more philosophical?
That said, the lectures are pretty rough around the edges compared to something like a coursera class. And his goal is to get you to play around with stuff on your own using his stuff as a jumping off point. It works well for some people but not as much for others.
Multiple times, the prof said things like "the theory says this shouldn't work, but it does, so we use it." (Thinking about his perspective on the practical reality surrounding the "curse of dimensionality" (i.e. that it's not a curse)
1. ConvNet (for images and structured data)
2. RNN (for text and sequence data).
They don't teach you far advanced stuff, like how to creatively misuse the hammers to knock different nails (example: using ConvNet, tweak as causal convolution to handle sequence data)
In light of that, I believe the technology to have progressed to where one can learn how to use existing libraries to solve specific problems. Lots of businesses have specific problems, and will pay me to solve them.
The Fast.AI course (part 1 v2) is the best way to get started, IMO. The fastai library wraps up a lot of boilerplate, and gives you a simple recipe w/ conceptual understandings to achieve state of the art results (top 20% on just about any kaggle competition) in just a couple months of intensive study.