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it takes time, work, lots of trial-and-error to find which learning style works best for you.

https://www.youtube.com/watch?v=vJo7hiMxbQ8 autoencoders

https://www.youtube.com/watch?v=x6T1zMSE4Ts NVAE: A Deep Hierarchical Variational Autoencoder

https://www.youtube.com/watch?v=eyxmSmjmNS0 GAN paper

and then of course you need to check the Stable Diffusion architecture.

oh, also lurking on Reddit to simply see the enormous breadth of ML theory: https://old.reddit.com/r/MachineLearning/search?q=VAE&restri...

and then of course, maybe if someone's nickname has fourier in it, they probably have a sizeable headstart when it comes to math/theory heavy stuff :)

and some hands-on tinkering never hurts! https://towardsdatascience.com/variational-autoencoder-demys...




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