The one thing I see over and over and over again, in these articles and associated discussion, is failure to recognize the distinction between "ML research" and "applied ML". You always get people in the threads yelling "you can't do ML without graduate level knowledge of measure theory, real analysis, information theory, ..." and so on, ad infinitum. And that is very likely true for most ML
research. But OTOH, you can absolutely take off the shelf libraries, the level of knowledge you would get from, say, taking Andrew Ng's Coursera class, and then create value for your company using ML. No measure theory or linear algebra needed.
And of course all this happens along a spectrum from "requires the least theory/math" to "requires the most theory/math". It's not a strictly binary dichotomy.
So is the person completing the fast.ai course going to be the inventor of the next great new NN training algorithm? Maybe not. Probably not, even. But are they going to be able to apply ML to solve real problems? Yeah, most likely.
And of course all this happens along a spectrum from "requires the least theory/math" to "requires the most theory/math". It's not a strictly binary dichotomy.
So is the person completing the fast.ai course going to be the inventor of the next great new NN training algorithm? Maybe not. Probably not, even. But are they going to be able to apply ML to solve real problems? Yeah, most likely.