Normalization, which rescales inputs or layer outputs to a common range (e.g., mean 0, variance 1)

 Normalization, which rescales inputs or layer outputs to a common range (e.g., mean 0, variance 1), is a form of numerical hygiene: it averts vanishing/exploding gradients and speeds convergence. Typical methods—feature standardization, min-max scaling, BatchNorm, LayerNorm—do not inherently fight over-fitting, though BatchNorm’s fluctuating batch statistics add light dropout-like noise that can shave a few points off test error. The real antidote is regularization: L1/L2 weight decay constrains parameter magnitude; dropout randomly silences units; data augmentation enlarges the effective dataset; early stopping halts training once validation loss rises; label smoothing and Mixup also help. A pragmatic four-step recipe is (1) always normalize inputs for stability, (2) layer multiple regularizers, (3) trim depth or width when the train–validation gap stays wide, and (4) track that gap with a sound validation split to ensure generalization.