dailyai.report

The 2016 paper by Zhang et al. challenged classical deep learning theory by demonstrating that models could fit random labels perfectly. This finding contradicted existing beliefs about generalization and model complexity. It forced researchers to abandon old optimism. Practitioners now face a persistent gap between empirical success and theoretical understanding of neural networks.