Training deep neural networks can be challenging. As parameters in earlier layers change during training, the distribution of inputs to subsequent layers also shifts. This phenomenon, known as internal covariate shift, can slow down training and make it sensitive to initialization and learning rates.

This chapter focuses on normalization techniques designed to address these issues. We will examine:

• The concept of internal covariate shift and its effects on training dynamics.
• Batch Normalization: How it works by normalizing layer inputs using mini-batch statistics ($\mu$ and $\sigma^2$), its forward and backward pass calculations, and its behavior during inference.
• The benefits of Batch Normalization, including faster convergence, improved stability, and a slight regularization effect.
• Practical considerations for implementing Batch Normalization in neural networks.
• A brief look at Layer Normalization as an alternative approach.

By the end of this chapter, you'll understand how normalization layers contribute to more stable and efficient deep learning model training.