Batch Normalization Explained | Why It Works in Deep Learning

In this video, we dive into Batch Normalization in deep learning, unpacking not just how batch normalization works but also why it works. Batch Normalization has become one of the most influential techniques in training deep neural networks and convolutional neural networks (CNNs). But what is Batch Normalization in neural networks, and what makes it so effective? We start with the motivation, why normalizing inputs to a neural network matters, and how it improves learning by stabilizing and reshaping the optimization landscape. From there, we explore the internal mechanics of the Batch Normalization layer, including how it transforms intermediate values using mean and variance, and how scale and shift parameters are learned. A common talking point regarding batchnorm is whether to have Batch Normalization before or after non-linearity, so we go over that a bit and finally break down how it behaves differently during training versus inference — and what that means for the model’s forward pass. Once we cover core Batch Normalization parts, we then go over some of the important findings from other papers which try to reason on its effectiveness and find reasons to explain batchnorm success. The papers mainly cover how Batch Normalization improves gradient flow, leads to smoother loss landscape, helps mitigate vanishing and exploding gradients, and enables higher learning rates and faster convergence. ⏱️ Timestamps 00:00 Intro 00:28 Standardizing Input Features 03:49 Internal Covariate Shift 05:44 Transforming Layer Inputs using Batch Normalization 08:49 Batch Normalization before or after activation function 11:12 Scale and Shift Parameters in Batch Normalization 13:25 Training and Inference of Batch Normalization Layer 18:42 BatchNorm Results and Benefits 23:57 Paper Overview : Understanding Batch Normalization 26:50 Paper Overview : How Does Batch Normalization Help Optimization ? 33:43 Paper Overview : Batch Norm Biases Residual Blocks Towards Identity 37:54