Why Machine Learning Feels Like Guesswork

I've been messing with model training again and realized (for the 10th time) how much of it still feels like guessing. Change the learning rate a bit and things explode. Normalize differently and the model starts working like magic. None of this feels deterministic. It's like... you're tuning chaos.

It's funny, we dress this stuff up in math and papers and graphs, but under the hood it's still a lot of trial and error. Anyone who says otherwise either hasn't trained a model end-to-end or forgot how weird it is when you actually start doing it.

I'm not saying we should throw out theory. I'm saying theory doesn't save you from weird bugs and tuning pains. And maybe that's part of the fun.