> Practical/applied stuff seems to require levels of compute, training data, etc. that make it unapproachable for a home hacker for everything beyond basic exercises like a handwritten digit classifier.
Compute is only an issue if you want to train large foundational models from scratch. You can easily train non-neural models on CPU, and fine-tune smaller models (say <4B) on a commodity GPU.
For training data there's lots of datasets online (like Kaggle), but you can build your own and can label things yourself or get a language model to label them for you. Getting good data is the hardest part of ML so this is a good exercise.
Then do some hobby projects: classify whether your photos contain a photo of you or a particular friend/family member, predict the expense category from bank transaction exports, classify your emails into categories, extract book recommendations from Hacker News, ...
Compute is only an issue if you want to train large foundational models from scratch. You can easily train non-neural models on CPU, and fine-tune smaller models (say <4B) on a commodity GPU.
For training data there's lots of datasets online (like Kaggle), but you can build your own and can label things yourself or get a language model to label them for you. Getting good data is the hardest part of ML so this is a good exercise.
Then do some hobby projects: classify whether your photos contain a photo of you or a particular friend/family member, predict the expense category from bank transaction exports, classify your emails into categories, extract book recommendations from Hacker News, ...