What is meant by Batch Normalization in Deep Learning?
Batch Normalization is a technique used in deep learning to standardize the inputs of each layer, ensuring stable training by reducing internal covariate shifts and accelerating convergence. It involves normalizing the activations with mean and variance calculated over mini-batches, along with learnable parameters for scaling and shifting.
Applying Batch Normalization in Keras using BatchNormalization Class
Training deep neural networks presents difficulties such as vanishing gradients and slow convergence. In 2015, Sergey Ioffe and Christian Szegedy introduced Batch Normalization as a powerful technique to tackle these challenges. This article will explore Batch Normalization and how it can be utilized in Keras, a well-known deep-learning framework.