Batch Normalization

Batch Normalization is a technique used in deep learning neural networks to improve the training speed and stability of the model. It works by normalizing the inputs of each layer, which helps to reduce the internal covariate shift problem. This problem occurs when the distribution of the inputs to a layer changes during training, which can make it difficult for the model to converge. Batch Normalization addresses this issue by normalizing the inputs to each layer based on the mean and variance of the inputs in the current batch. This helps to ensure that the inputs to each layer have a similar distribution, which can improve the stability and speed of training. Additionally, Batch Normalization can act as a form of regularization, which can help to prevent overfitting and improve the generalization performance of the model.
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