#Normalization

Batch Normalization is the magical ritual that temporarily freezes the egotistical variance of data known as internal covariate shift in neural networks, calming training in the short term. Hailed as the savior of stability, in practice it creates a new swamp of hyperparameters, inflicting stomach cramps upon researchers like a sarcastic deity. Bound by the shackles of batch size, it enforces collective responsibility across layers. Disguised as the universal remedy, it ironically spawns fresh problems, embodying the ultimate AI-era trick.