A clear and engaging explanation of bias, variance, underfitting, overfitting, and why building good machine learning models is really about finding the right balance.
OpenAI researchers accidentally left a model training over the holidays. When they came back, a model that had completely failed on unseen data now understood everything perfectly. This is the story of grokking—and why it quietly broke several rules we thought were permanent.
Overfitting is the classic bait-and-switch of machine learning: dazzling training accuracy that vanishes on unseen data. Learn what it is, why it happens, how to spot it, and when it might even be useful.