#batch normalisation

Momentum and Normalization in Deep Learning Comparing Momentum and Normalization in Deep Learning: A Mathematical Perspective Momentum and normalization are two pivotal techniques in deep learning that enhance the efficiency and stability of training. This article explores the mathematics behind these methods, provides examples with and without these techniques, and demonstrates why they are…

Understanding Trainable and Non-Trainable Parameters in Batch Normalization Demystifying Trainable and Non-Trainable Parameters in Batch Normalization Batch normalization (BN) has become a crucial component in the deep learning toolkit, particularly for training deep neural networks. However, the concepts of trainable and non-trainable parameters in BN often confuse practitioners, especially…

Introduction to Batch Normalization Batch normalization is a widely used technique in deep learning that significantly improves the performance and stability of neural networks. Introduced by Sergey Ioffe and Christian Szegedy in 2015, this technique addresses the issues of vanishing and exploding gradients that can occur during training, particularly in deep networks. Why Batch Normalization? In…