Neural Networks

Batch Normalization: Mathematical Foundations, Gradient Smoothing Dynamics, and Why Sequence Models Adopted Layer Normalization

Batch Normalization remains one of the most widely implemented algorithmic developments in the history of deep learning. Introduced by Sergey Ioffe and Christian Szegedy in their 2015 paper, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, the technique enabled stable training of deep feedforward networks and convolutional architectures at significantly higher learning rates. While initially designed for computer vision architectures such as ResNet a