> Please, please: learn some probability via measure theory. [And integration]
Are there any good, gentle books on measure theory for self-study?
Just know stuff (or, how to achieve success in a machine learning PhD)
61–70 of 108 posts
Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#62Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#63This was a really fun list to read through. I agree with the author that knowing as much as possible about how things work, not just what things do, is extremely useful. However, "Just know stuff" I think is a secondary requirement (although an important one) to be successful. People really struggle to "just know stuff" if they aren't interested in the subject in the first place. People who aren't interested will set…
This is a great point I didn't cover!
"Just know stuff" tends to follow naturally from "care about stuff".
Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#64I keep searching how to be a good researcher but I haven't found any answers. I know almost all of this except for weirdly arbitrary/specific stuff (you really don't need to know Haskell for an ML PhD). It's all pretty basic, half of it you learn during any CS Masters degree, the other half are models and concepts that have been popular enough in the last few years that you would have read the papers and possibly imp…
I'm sorry to hear that things didn't go so well for you!
FWIW being able to "program like nobody's business" is still really really valuable. It's why I dedicate such a large chunk of the post to software dev skills. :)
Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#65Earlier quoted context omitted.
Clearly there are people that don’t need to know math. You happen to be one of them, congratulations. Though I know I’d be bored out of my mind if I did software work that only used high school level math and logic.
Sure, some people need it, I was just responding to: > I'm still appalled at how people can manage to gather the courage to utter they don't need math. When... well, the vast majority of people really don't. They promptly forget almost everything back to about 6th grade math, shortly after finishing formal education, because they truly never need it , so that knowledge and those skills quickly rust. If these people i…
I see what youre saying. I don't think it's a failure of the curriculum to teach people things when they are young they don't end up using. Certainly a 13 year old isn't going to know what their future career path/interests will be (some do, but most don't) and shouldn't let them shut doors down the road at such a young age. I think at this point high school has devolved to the point of just giving everyone the basic broad skills that they could feasibly succeed at any college major. The seniors that have already decided they just want to build houses all day can complain during math, we all heard it, "when will I ever use this", but the problem is the answer is not "never" and its not "always" its "we don't know, but you may need it, and closing those doors now will limit your future potential".
Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#66Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#67Earlier quoted context omitted.
Should they be? In this case wouldnt it end up being false modesty? Like, if this person cant say "Look at me, I am UNUSUALLY INTELLIGENT!" then who can?!
> Like, if this person cant say "Look at me, I am UNUSUALLY INTELLIGENT!" then who can?! No one, that's my point. If academia has not taught to the author that his intelligence isn't unusual, the workforce of his new employer certainly will. Listing github stars and twitter followers in the second paragraph as an achievement to me transpires lack of maturity and a need for external validation. On the bright side, bei…
I'm sorry it came across this way for you! Rather, I'm just outlining why folks seem to keep asking me this question. :)
Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#68Earlier 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…
This is a great description, thank you! What you've said is precisely the reason I emphasised knowing a bit of foundational math, e.g. topology.
Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#69Earlier quoted context omitted.
Agreed that it would be nice, but exactly how much of the work of becoming educated are you willing to ask this person to do on your behalf?
Yeah, but perhaps they should have written/published this in a form such that others can add references.
Holy hell, thats an idea with legs. Why dont you make that and send it their way?
Re: Just know stuff (or, how to achieve success in a machine learning PhD)
#70Thinking about this, I'd be also interested to hear what the author learned and didn't find useful over his phd. Is this a list of most of what he ended up learning, (which could potentially then have a lot of conformation bias in it) or is it curated from the maze of blind alleys he went down?
Ooh, that's a great suggestion!
So one thing I learned a lot of in my PhD (for literally a whole year), that I literally never needed, was functional analytic methods for PDEs. Stuff like Moser iterations / the De Giorgi-Nash-Moser theorem, etc.
The finer details of Turing machines have never really helped me, although in my case that's probably the exception as I imagine that's still pretty important.
On a more ML note, I have literally never needed SVMs. (And hope I never get asked about them, I've forgotten everything about them haha.)
I think there's a lot of other stuff I could add to the "just-don't-know-stuff" list!
(And to answer your last question: this list is curated, and based on the criteria of (a) is it useful, and (b) is it widely applicable.)