[smallcharleston]

I feel somewhat similarly. If you want to learn ML from the “ground up” that means learning math (at least a few subjects) to the senior undergraduate level, some numerical methods, some probability and statistics, sprinklings of other stuff before you even get to the models. And it’s not even clear that stuff is important for ML in practice.

I’m someone who took all those math courses and some grad ML coursework. And what that means is that I’m qualified to try and hack together some specific research level things that a practitioner will be confused by, and then try to write a paper about it. It doesn’t mean I’m qualified to do what the practitioner does. Frankly I never ran my code on anything other than MNIST yet and don’t know the different architectures or applications well, since they’re not directly what I work on. They’re just different things, as I see it.