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I agree with this advice once you get to a sufficiently high level -- like, working on research problems. HOWEVER I think there's more nuance: there is tremend
by JustinSkycak 2y ago
I agree with this advice once you get to a sufficiently high level -- like, working on research problems.
HOWEVER I think there's more nuance: there is tremendous value in having a baseline level of foundational knowledge in whatever domain you're in.
For instance, if someone hasn't learned math to an undergraduate level, and they try to sink their teeth into a math research problem, then they're probably going to spend all their time flailing around and being confused. Waste of time.
A better use of time would be for them to develop a baseline level of mathematical knowledge through a structured curriculum, and then take the leap once they've got their fundamentals down.
Same thing with machine learning. If you want to work on a research problem and you have your ML fundamentals down, then yeah, go ahead and jump right into the research problem. But if you don't even know how gradient descent works, or what an eigenvalue is, how to work with continuous probability distributions, etc., then you're woefully underprepared and you'd be better off just shoring up your foundations first.
- lallysingh 2y agoMeh? I think a more mercenary approach to learning prerequisites is fine, and frankly better. Use Mathematica or Alpha to show you how equations get from one place to another - those tools are great at the fundamentals. You can skim a calc book, use Mathematica/Alpha to fill in your gaps, and play with toy problems to convince yourself well enough to continue. The basics are the ones with the most different approaches developed to teach/use them, so they can most easily be skimmed by with cheap modern trickery.
- JustinSkycak 2y agoThere are some people who can pull that off, and you might be one of them, but they're extreme cases. For the vast, vast majority of students it's not a viable approach.