What is Batch Normalization?
Batch Normalization (BN) is a technique used in deep learning to improve the training of neural networks. It was introduced to address issues related to internal covariate shift, which occurs when the distribution of inputs to a layer changes during training. This can make the training process slow and unstable.
The primary goal of Batch Normalization is to normalize the output of a previous layer by adjusting and scaling the activations. This is done by calculating the mean and variance of the batch data and then using these statistics to standardize the inputs. Each mini-batch is normalized, allowing for faster training, improving convergence rates, and enhancing the overall performance of the models.
Additionally, BN introduces two learnable parameters: scale (gamma) and shift (beta), which allow the network to retain the capacity of representation. Layer normalization can also be applied to different network architectures, ensuring compatibility with various systems and improving robustness against different training conditions.
In summary, Batch Normalization is a powerful technique that helps stabilize and accelerate the training of deep neural networks, making it a widely adopted practice in artificial intelligence and deep learning applications.