21–30 of 108 posts
If anyone's reading over this and feels "Gosh, I'll never be an ML dev; this is way too much": I don't know most of that list, and still manage to be a productive researcher. I learn what I need as I go. That's probably the optimal strategy. I'm skeptical of first-principles learning. It's great to immerse yourself in theory, but when you've gone all the way to "topology" you've probably gone beyond the limit of what…
FWIW very few of the ML devs I work with have PhDs. I'm not sure aspiring ML devs are the intended audience.
If anyone's reading over this and feels "Gosh, I'll never be an ML dev; this is way too much": I don't know most of that list, and still manage to be a productive researcher. I learn what I need as I go. That's probably the optimal strategy. I'm skeptical of first-principles learning. It's great to immerse yourself in theory, but when you've gone all the way to "topology" you've probably gone beyond the limit of what…
Researchers absolutely need to know a lot, not necessarily all the way to topology or w/e but definitely the underlying mathematical principles in order to advance the field (IMO).
Earlier quoted context omitted.
> I'm still appalled at how people can manage to gather the courage to utter they don't need math. I try to (re-)learn math periodically because I feel like I should, like how one ought to eat one's vegetables and one ought to exercise, but extrinsic motivation is the thing that's lacking. I usually end up on recreational math puzzles or something, before dropping it, since at least those are fun. Probably made three…
It’s one of those things where if you don’t have the math knowledge, the opportunities to apply it will be literally invisible to you. If all you have is a hammer, everything looks like a nail—but the converse of that is that if you have never seen a hammer, nails will be invisible and incomprehensible to you, they will just blend into the background of noise. When I learn about something, I suddenly see it everywher…
Maybe I should start a PhD in ML as a university drop out, it sounds like a list of basic stuff taught within the first two years of any math/CS program, but I might miss something as it's far from being detailed...
Now is a terrible time to start a PhD in ML. If you do a CS PhD, pick literally any other subfield.
> Please, please: learn some probability via measure theory. You’ll start reading machine learning papers wondering how people ever express themselves precisely without it. The entire field seems to be predicated around writing things like x ~ p_\theta(x|z=q_\phi(x)) as if that’s somehow meaningful notation. Hear hear! How did ML get saddled with such awful notation?
I would add the need for geometry and topology becoming more of a pre-requisite for picking up modern methods: https://arxiv.org/abs/2104.13478