BatchNormalization
BatchNormalization is a technique used in neural network training to improve model stability and accelerate convergence. Introduced by Sergey Ioffe and Christian Szegedy in 2015, it normalizes the inputs of each layer by adjusting and scaling the activations, addressing issues related to internal covariate shift.
The process involves standardizing the inputs to each mini-batch during training, subtracting the batch mean and
BatchNormalization is typically applied after the linear transformation and before the activation function within a layer.
The benefits of BatchNormalization include reduced sensitivity to weight initialization, allowing for higher learning rates, and
However, BatchNormalization also has limitations, such as decreased effectiveness with very small batch sizes, where batch
Overall, BatchNormalization is a fundamental technique that has significantly contributed to the advancement of deep learning