I actually tried with this code! No, it didn't work. My explanations are that 1) the forward walk noise has a certain character that is part of the learning, and 2) what I see is that other data does move, but it just moves in tandem too much and the points don't split up enough to cover the whole area. I suppose it fails because close points move together. (As for my 2), one should on the other hand also not try to view the points as individuals.)
learning sprintboot right now, don't distract me from AI huh. Jk. Can you share some stuff to start learning GenAI from scratch(mathematics itself), interning at a company CRM company right now, they also have a ML team, might be able to join if shown some skills. You can share advices also.
It's not like I got a list of strong references - I'm trying to dig for them myself right now. One recent pdf I came across is "Tutorial on Diffusion Models for Imaging and Vision" by Stanley Chan - maybe there's merit to it. If you know the math of the Ornstein-Uhlenbeck process, , which has independent interest and so is also covered e.g. on math books on stochastic differential equations, then the ML part might mostly be plug and chuck and wondering what exactly it is that makes the learnability possible. I feel a lot of people with that topic in particular tie in to physics, so if you've ween the Langevin equations that might help too. But while I love to get lost in theory, with this stuff I feel is it's good to just hack into a python script for starters, like I did here. Hope that helps. PS let me know if you find something thorough too.
![รวมเพลงลูกทุ่ง 6 หนุ่มไทบ้าน ฮิตติดกระแส ชุดที่ 1 l มนต์แคน ไมค์ ไผ่ เสถียร ไหมไทย พี [Longplay]](http://i.ytimg.com/vi/GD2v1OjMw5I/mqdefault.jpg)
The chad man does it again. Thanks man, I'll be using that repo to look more interesting.
looks like solution to optimal transport problem
Gabriel Peyre has entered the chat
do you think its possible to diffuse stuff that isnt gaussian noise as a starting point? so for example from a t-rex to a butterfly
I actually tried with this code! No, it didn't work. My explanations are that 1) the forward walk noise has a certain character that is part of the learning, and 2) what I see is that other data does move, but it just moves in tandem too much and the points don't split up enough to cover the whole area. I suppose it fails because close points move together. (As for my 2), one should on the other hand also not try to view the points as individuals.)
Nice ty
learning sprintboot right now, don't distract me from AI huh.
Jk. Can you share some stuff to start learning GenAI from scratch(mathematics itself), interning at a company CRM company right now, they also have a ML team, might be able to join if shown some skills. You can share advices also.
It's not like I got a list of strong references - I'm trying to dig for them myself right now. One recent pdf I came across is "Tutorial on Diffusion Models for Imaging and Vision" by Stanley Chan - maybe there's merit to it. If you know the math of the Ornstein-Uhlenbeck process, , which has independent interest and so is also covered e.g. on math books on stochastic differential equations, then the ML part might mostly be plug and chuck and wondering what exactly it is that makes the learnability possible. I feel a lot of people with that topic in particular tie in to physics, so if you've ween the Langevin equations that might help too. But while I love to get lost in theory, with this stuff I feel is it's good to just hack into a python script for starters, like I did here. Hope that helps. PS let me know if you find something thorough too.