Part 4/10:
A groundbreaking moment emerged in 2019 when researchers revealed a curious behavior: when training neural networks, the test error often begins to decrease even after the model has completely memorized the training data, a phenomenon dubbed "double descent." In essence, as model size continues to increase beyond the point of overfitting, its ability to generalize begins to improve again. This unexpected finding challenges the fundamentals of statistical learning theory, raising questions about how larger models could outperform their smaller counterparts.