dailyai.report

Deep neural networks often struggle with vanishing gradients and training instability as layer counts increase. This curse of depth forces researchers to implement complex normalization and initialization schemes to maintain signal flow. Practitioners must balance model capacity against these optimization hurdles. Over-parameterization does not automatically solve these fundamental architectural bottlenecks.