Comment by fantispug

> 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, ...