Batch Normalization
Batch Normalization is a technique used in deep learning to normalize the inputs of each layer in a neural network by adjusting and scaling the activations. It reduces internal covariate shift by standardizing the outputs of a layer to have zero mean and unit variance, which stabilizes and accelerates training. This method is commonly applied before or after activation functions in layers like convolutional or fully connected ones.
Developers should learn Batch Normalization when building deep neural networks, especially for tasks like image classification, object detection, or natural language processing, as it allows for higher learning rates, reduces overfitting, and improves model convergence. It is particularly useful in complex architectures like ResNet or Inception, where training deep networks can be challenging due to vanishing or exploding gradients.