1. It depends, if you just want to <i>use</i> some model and call APIs, then you do not have to learn any ML theory. You just have to learn using libraries following their GitHub Readme instructions. Get a Colab Pro+ subscription or run Kaggle Notebooks for free. You can also simply use GUIs built on top of Open Source models.<p>2. Learn to use the Hugging Face library, and use their stuff on your Notebooks.<p>3. Learn some ML theory so you can understand hyperparameters better, and can tweak them in a better way.<p>____<p>If you want to get into training models by yourself from scratch, you have to learn in a deeper manner, and cannot overlook learning ML theory in a deeper manner.<p>____<p>The most obvious ways would be:<p>1. Looking into stuff that John Whitaker does [0] and his elaborate free course on AI Art [1].<p>2. Learning ML from scratch starting from Andrew Ng ML, then going to DL, then learning about GANs.<p>3. Learning from fast.ai through their two-part course on Deep Learning, where Stable Diffusion is now being taught. Then learn PyTorch from another place like Sebastian Raschka's book.<p>4. Watching old videos from Stanford CS231n when Karpathy was a TA, and taught in the class. Then Deep Dream was standard.<p>_____<p>If you are a responsible, mature person, and you are in it for the long term, and have deep pockets, buy some GPU. 2x 3090 is reasonable, and should be enough.<p>____<p>Let me know if you have any further questions.<p>[0]: <a href="https://datasciencecastnet.home.blog/" rel="nofollow">https://datasciencecastnet.home.blog/</a><p>[1]: <a href="https://youtube.com/playlist?list=PL23FjyM69j910zCdDFVWcjSIKHbSB7NE8" rel="nofollow">https://youtube.com/playlist?list=PL23FjyM69j910zCdDFVWcjSIK...</a>