What is batch normalization in deep learning?

Experience Level: Junior
Tags: Machine learning


Batch normalization is a technique used in deep learning to improve the training of artificial neural networks. It involves normalizing the activations of the previous layer for each batch of data before applying the activation function. Batch normalization can accelerate training, reduce overfitting, and make the model more robust to changes in the distribution of the input data.
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