Comment by ramraj07
2 years ago
When you dive deep into the math, many things are fundamentally the same. Superresolution is just glorified deconvolution. A single layer perceptron is a linear kernel SVM is a logistic regression. FFT is just factorisation.
What does it take to learn math at such a deep level?
Even just undergraduate linear algebra & calc + real analysis & prob/stats... With good teachers to draw the connections
Stuff like info theory are amazing bc of this... But easy to miss if you are just working through a drier text
The crazier version for me is we have a physics professor on our team whose grounding intuitions are use ideas like black holes for mental intuition... Which does not work as well for the rest of the team
(And a lot of modern ML/AI feels very engineered and interchangeable after that, like 'metric function of the month')
Either a year in college, or your entire lifetime. Not that it really matters since mathematically, they're both fundamentally the same, they're both just numbers.
You don’t really need to go that deep, just broad
You can get a lot just by reading Wikipedia and following links in each article
It’s hard to grasp the formulas and proofs sometimes, but if you only care about understanding the concepts, there are a lot of dots to connect
Reading textbooks is wildly more effective than trying to pick up new math from Wikipedia.
decades of trying, in my case. and I still only understand a little. I focused a lot on linear algebra, graphs, and probability- most of abstract math is completely outside my understanding.
other folks pick it up quickly in college.
Numerical methods are Ax=B