The Math Behind Batch Normalization
Explore Batch Normalization, a cornerstone of neural networks, understand its mathematics, and implement it from scratch.
Batch Normalization is a key technique in neural networks as it standardizes the inputs to each layer. It tackles the problem of internal covariate shift, where the input distribution of each layer shifts during training, complicating the learning process and reducing efficiency. By normalizing these inputs, Batch Normalization helps networks train faster and more consistently. This method is vital for ensuring reliable performance across different network architectures and tasks, including image recognition and natural language processing. In this article, we’ll delve into the principles and math behind Batch Normalization and show you how to implement it in Python from scratch.
Index
1: Introduction
3: Math and Mechanisms
∘ 3.1: Overcoming Covariate Shift
∘ 3.2: Scale and Shift Step
∘ 3.3: Flow of Batch Normalization
∘ 3.4: Activation Distribution
4: Application From Scratch in Python
∘ 4.1: Batch Normalization From Scratch…
