erm, in english, please !
Comment on answer = sum(n) / len(n)
Socsa@sh.itjust.works 2 months ago
Bayesian purist cope and seeth.
Most machine learning is closer to universal function approximation via autodifferentiation. Backpropagation just lets you create numerical models with insane parameter dimensionality.
lseif@sopuli.xyz 2 months ago
hotsox@lemmy.blahaj.zone 2 months ago
Universal function approximation - neural networks. Auto-differentiation - algorithmic calculation of partial derivatives (aka gradients) Backpropagation - when using a neural network (or most ML algorithms actually), you find the difference between model prediction and original labels. And the difference is sent back as gradients (of the loss function) Parameter dimensionality - the “neurons” in the neural network, ie, the weight matrices.
If thats your argument, its worse than Statistics imo. Atleast statistics have solid theorems and proofs (albeit in very controlled distributions). All DL has right now is a bunch of papers published most often by large tech companies which may/may not work for the problem you’re working on.
Universal function approximation theorem is pretty dope tho. Im not saying ML isn’t interesting, some part of it is but most of it is meh. It’s fine.
kibiz0r@midwest.social 2 months ago
A monad is just a monoid in the category of endofunctors, after all.
AnarchistArtificer@slrpnk.net 2 months ago
No, no, everyone knows that a monad is like a burrito.
(Joke is referencing this: blog.plover.com/prog/burritos.html )
Contravariant@lemmy.world 2 months ago
So what you’re saying is that if you put a burrito inside a burrito it’s still a burrito?
AnarchistArtificer@slrpnk.net 2 months ago
feedum_sneedson@lemmy.world 2 months ago
pee pee poo poo wee wee
embed_me@programming.dev 2 months ago
Any practical universal function approximation will go against entropy.
hotsox@lemmy.blahaj.zone 2 months ago
Universal function approximation - neural networks. Auto-differentiation - algorithmic calculation of partial derivatives (aka gradients) Backpropagation - when using a neural network (or most ML algorithms actually), you find the difference between model prediction and original labels. And the difference is sent back as gradients (of the loss function) Parameter dimensionality - the “neurons” in the neural network, ie, the weight matrices.
If thats your argument, its worse than Statistics imo. Atleast statistics have solid theorems and proofs (albeit in very controlled distributions). All DL has right now is a bunch of papers published most often by large tech companies which may/may not work for the problem you’re working on.
Universal function approximation theorem is pretty dope tho. Im not saying ML isn’t interesting, some part of it is but most of it is meh. It’s fine.
TopRamenBinLaden@sh.itjust.works 2 months ago
I like your funny words, magic man.