Batch Normalization is a technique used to improve the training of deep neural networks by normalizing the inputs of each layer. This process helps mitigate the problem of internal covariate shift, where the distribution of inputs to a layer changes during training, leading to slower convergence. In essence, Batch Normalization standardizes the input for each mini-batch by subtracting the batch mean and dividing by the batch standard deviation, which can be represented mathematically as:
where is the mean and is the standard deviation of the mini-batch. After normalization, the output is scaled and shifted using learnable parameters and :
This allows the model to retain the ability to learn complex representations while maintaining stable distributions throughout the network. Overall, Batch Normalization leads to faster training times, improved accuracy, and may reduce the need for careful weight initialization and regularization techniques.
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