2015 Paper on Batch Normalization

The 2015 paper on Batch Normalization introduces a technique that improves deep neural network training by normalizing the inputs of each layer during training. This helps stabilize the learning process, allowing for faster training, higher learning rates, and reduced sensitivity to initial settings. Essentially, it adjusts the data passing through the network to maintain consistent distribution, preventing issues like vanishing or exploding activations. Batch Normalization enhances model performance and makes training more efficient, contributing significantly to advances in deep learning practices.