Trying to dive into machine learning until mathematics stops me cold
Description
Two stacked movie-still frames form a meme. In the first frame, a casually dressed man exiting a modern glass-door café is labeled with large white text “Me”; he clutches his chest while admiring a woman walking past who is labeled “Machine Learning.” In the second frame, a different man in a plaid shirt plants his hand firmly on the first man’s chest, blocking his path; this blocker carries the white label “Mathematics.” The background shows polished metal doorframes, reflections in the glass, and urban sidewalk scenery. The joke plays on developers’ eagerness to jump into trendy ML tooling only to be confronted by the prerequisite of solid mathematical foundations - linear algebra, calculus, and probability - before making meaningful progress
Comments
30Comment deleted
Machine Learning looks like the perfect mid-career fling - until mathematics shows up as the bouncer demanding the linear-algebra ID I misplaced around JDK 1.4
After spending three months debugging a neural network that wouldn't converge, you finally understand why your linear algebra professor kept saying 'this will be useful someday' - turns out, eigenvalues weren't just academic torture, they're why your PCA is crying and your optimizer is having an existential crisis
Every ML engineer's journey: spending months tuning hyperparameters with frameworks they barely understand, only to realize that Mathematics has been standing there the whole time, patiently waiting to explain why their model actually works - or more often, why it spectacularly doesn't. Turns out you can't just import numpy and call it a day when your gradient descent is converging to the wrong local minimum because you skipped the calculus lecture on convex optimization
Everyone wants to skip to 'import sklearn; model.fit', but the moment it diverges, the linear algebra bouncer demands ID: eigenvalues, gradients, and a convergence proof
About to “do ML,” then Mathematics grabs me: “Define the loss, check identifiability and condition numbers, and stop pretending random_state=42 is a methodology.”
ML: the shiny microservice. Math: the monolith dependency you can never migrate away
но математика ведь топ😢 Comment deleted
please speak english in this chat or provide a translation alongside your original text Comment deleted
also, I do agree with that statement (maths is top) Comment deleted
"Слишком математично, я зря родителям говорил, что математика в программировании не нужна и что я справлюсь без универа...." "Too mathematical, I was wrong to tell my parents that mathematics is not needed in programming and that I can do it without a university degree ...." Comment deleted
mate Comment deleted
bruh google translate got stuck Comment deleted
Those are chat rules. If you violate them, you'll get kicked. I didn't make 'em Comment deleted
no problem Comment deleted
and you don't enforce them either - please don't argue with people over it when a mod is here Comment deleted
I'm not arguing, just explaining, bro Comment deleted
Nah, that didn't look like an explanation, but nvm, I got it Comment deleted
Sorry if I sounded harsh. That wasn't my goal Comment deleted
also not needed - that's what us mods are for. @pavel_the_best knows the rules, he's been active in this chat for quite some time. Comment deleted
Cool, ok Comment deleted
speak English or provide a translation Comment deleted
that's pinned message in a Russian chat about algorithms Comment deleted
which chat? Comment deleted
oh, fuck Comment deleted
no worries, just remember this rule :) Comment deleted
dude, I understand u Comment deleted
I think it's mainly statistics right? Or are there other branches of math that ml uses? Comment deleted
If you're going for regression or classification models, you might want to review your linear algebra and calculus notes Much of the math there is based on matrices and partial derivatives Comment deleted
Also calculus and linear algebra Comment deleted
learn js, make money without math Comment deleted