I feel really excited to experiment on such projects in my spare time but reading how it costs researchers hundreds and thousands of dollar don't even try.
The good news is that there's been huge progress in GAN efficiency over the past few years, and we are rapidly approaching the point where ImageNet-scale datasets can be reasonably trained with 1 GPU-month or less.
Worth noting in that vein are VQ-GAN https://compvis.github.io/taming-transformers/ and not-so-BigGAN https://arxiv.org/abs/2009.04433 . There's also a resurgence of VAE work which is relatively efficient: VD-VAE https://arxiv.org/abs/2011.10650#openai and NVAE https://arxiv.org/abs/2007.03898#nvidia ; DALL-E https://openai.com/blog/dall-e/ also appears to be surprisingly lightweight on the VAE side.
So, if you feel left out, don't worry. In the near future, between the RTX GPUs and optimization and new VAE/GANs, I expect things like Danbooru2020 https://www.gwern.net/Danbooru2020 to be totally doable on hobbyist hardware.
Somebody recommend Andrew NG courses but I wasn't able to complete it and got bored. Then I tried watching sentdex and it was amazing and I'm able to fully comprehend what he does since he teaches more practical aspects like running gta v on autopilot etc.
So just wondering what I could learn next.