I 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 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.
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. :)
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.