Having reviewed recurrent architectures in the previous chapter, we now turn to the Transformer model. Recurrent models process sequences token by token, posing challenges for parallel computation and modeling long-distance relationships. The Transformer architecture overcomes these limitations by replacing recurrence entirely with attention mechanisms.

This chapter dissects the Transformer's structure. You will learn about:

• The Scaled Dot-Product Attention mechanism, the core building block using queries, keys, and values ($Q$, $K$, $V$).
• How Multi-Head Attention allows the model to jointly attend to information from different representation subspaces.
• Techniques for incorporating sequence order using Positional Encoding, as self-attention itself is permutation-invariant.
• The structure of the Encoder and Decoder Stacks, composed of attention and feed-forward layers.
• The function of Layer Normalization and Residual Connections in stabilizing training for deep networks.

By the end of this chapter, you will understand the fundamental components that enable the Transformer's effectiveness in sequence modeling tasks.