Articles tagged as "generalisation"

Generalisation in deep learning asks the question of “how well will my AI/deep learning model do in the wild?”, or, “how much did my model actually learn about the real world, rather than just about the data it was trained on?”

Scaling laws are empirical formulae that relate two or more measurable quantities of a system. In deep learning, “scaling law” most commonly refers to the relationship between the amount of data and computation (model capacity) we put into a training run, vs. the generalisation error we get on some held-out test data.