Batch Normalization (BN) is a technique used in deep learning to improve the training of neural networks. It normalizes the inputs to each layer by adjusting and scaling the activations. This process helps stabilize the learning process and accelerates convergence, making it easier to train deep networks effectively. BN works by computing the mean and variance of the inputs over a mini-batch, allowing the model to maintain a consistent distribution of activations throughout training ³⁴.

One of the key benefits of Batch Normalization is its ability to mitigate the internal covariate shift, which refers to the changes in the distribution of network activations due to the updates in the parameters during training. By normalizing the inputs, BN helps to smooth the optimization landscape, allowing for more aggressive learning rates and potentially leading to better performance ⁵.

However, BN has some limitations, such as its dependence on batch size, which can restrict its application in certain scenarios. Alternatives like Group Normalization have been proposed to address these issues ².