51–60 of 108 posts
The tone of the post is pretty off putting. It reads as a "look how smart I am!" article - the author doesn't even pretend to be modest.
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?!
What you need to know depends largely what you are working on - and that also holds for a successful Ph.D. candidate. Of course, it is good advice to be open-minded beyond one's narrow field of inquiry, as the OP suggests, but a long list of maths topics may not be helpful a lot to beginners. The Ph.D. period is the time when you have some time available to acquire additional skills, and my advice is: try to strength…
I'm gonna be real as someone in grad school. Basically no PhD, grad students, or professors I know read full text books. I hear these ideas a lot, and they often sound like one of those Instagram influencer diets, that's completely unreasonable if you have any constraints in your life, and it's mostly been used to gate keep "real" scientists. Be well studied and knowledgeable about your problem domain, but you have a finite lifespan, so never feel bad that you aren't "educated enough".
I am not sure I can keep so many things active in my memory + also other things, depending on what I am working on in the last years. If my work in the last few years does not need some of this knowledge, I keep forgetting it.
Maybe my memory is just bad.
I think many of the things listed here are somewhat specific to what the author has worked on, so that it's easier for him to really know all this.
Maybe the point of this post is also more generic: Try to have an active memory over a diverse set of fields.
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.
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…
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 implemented them if you keep up with the field.
This hasn't helped me at all, my PhD has gone horribly and I'm not entirely sure why. The first year I wasted on an EEG-related review paper before realizing that EEG data is garbage, the second year I wasted on an original method that I wanted to pursue but it didn't perform well and got scooped. The third year (which ended up being the fourth year because of severe health issues) I was burnt-out and produced nothing of value.
I don't know how to produce good original research. I know how to program like nobody's business, better than any of my lab colleagues but this has done nothing to help me. I can implement a method in a night, I can implement tons of ideas in a year but if they don't perform better on the important metrics what's the point? I know the math and the ML side of things but this hasn't given me any insights, usually when I have an idea I realize it's already been done while doing the literature review.
It's all a big mystery to me, I think maybe the pressure of holding an industry job and having to finish the PhD before my funding ran out prevented me from experimenting freely but it might just be a convenient excuse.
The tone of the post is pretty off putting. It reads as a "look how smart I am!" article - the author doesn't even pretend to be modest.
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?!
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, being good at self-promotion when entering a mega-corporation will make sure he has a great career in front of him.
2 years? Sheesh. This is the type of stuff that makes me think genuis is a biological thing.