Training very deep neural networks, such as the multi-layer Transformers discussed previously, presents unique challenges. One significant factor affecting training stability and convergence speed is how the model's weights are initially set. Poor initialization can lead to problems like vanishing or exploding gradients, where the signal propagating through the network becomes too small or too large, hindering the learning process.

This chapter focuses on systematic approaches to weight initialization designed to mitigate these issues. We will examine established techniques that help maintain appropriate signal variance as data flows forward and gradients flow backward through deep networks.

You will learn about:

• The rationale behind principled weight initialization.
• Xavier (or Glorot) initialization, suitable for layers with symmetric activation functions like $tanh$.
• Kaiming (or He) initialization, tailored for layers using Rectified Linear Units (ReLU) and its variants.
• How to apply these methods specifically to the different components within a Transformer architecture, including embedding layers, attention mechanisms, and feed-forward networks.
• Special considerations for initializing the final layers of a model.

Understanding and applying these techniques is essential for successfully training the deep architectures required for large language models.