Batch normalization is a neural network technique that normalizes activations within training batches to improve training stability and efficiency. By adjusting and scaling intermediate values, it can reduce sensitivity to initialization, support higher learning rates, and help deep networks converge more reliably.
A technique for improving the performance and stability of artificial neural networks. It is a technique to provide any layer in a neural network with inputs that are zero mean/unit variance. Batch normalization was introduced in a 2015 paper. It is used to normalize the input layer by adjusting and scaling the activations.
Normalizing the input or output of the activation functions in a hidden layer. Batch normalization can provide the following benefits: - Make neural networks more stable by protecting against outlier weights. - Enable higher learning rates, which can speed training. - Reduce overfitting.