Description: A normalization layer is a layer in a neural network that normalizes the input data to improve the speed and stability of training. This process involves adjusting the distribution of neuron activations, helping to mitigate issues such as vanishing or exploding gradients. Normalization can be performed in various ways, with the most common being Batch Normalization and Layer Normalization. These techniques allow models to train faster and more effectively by stabilizing the distribution of inputs to each layer, resulting in more efficient learning. Additionally, normalization can act as a form of regularization, reducing the model’s dependence on weight initialization and improving generalization. In the context of machine learning frameworks, implementing normalization layers is straightforward and has become standard practice in designing modern neural network architectures. Its use extends to various applications, from computer vision to natural language processing, where the quality and speed of training are crucial for model performance.
