Fast.AI is 100% focused on getting into DOING stuff. Its guiding philosophy is that teaching the math first instead of teaching you how to actually do some machine learning is backwards. The lectures start with a dozen lines of code that learns how to discriminate between cats and dogs and then follows up on that by slowly removing layers of abstraction. It's very cool.

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.

Very focused on getting shit done, while trying to help you build appropriate mental models to what's going on. Towards the end of the course, some more theory.

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)

They teach you how to efficiently use (two kinds of) hammers and what nails to knock

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)