Batch Normalization is a technique in deep learning that normalizes the inputs of each layer within a neural network. By adjusting and scaling activations, it helps maintain the mean output close to 0 and the output standard deviation close to 1.
Key Benefits:
- Accelerated Training: Batch Normalization allows for higher learning rates and reduces the sensitivity to weight initialization, leading to faster convergence during training.
- Improved Stability: By normalizing activations, it reduces internal covariate shift, making the training process more stable.
- Reduced Need for Dropout: In some cases, Batch Normalization can eliminate the need for Dropout, a regularization technique used to prevent overfitting.