Having established the principles of self-attention and positional encoding, we now focus on how these elements are integrated within the complete Transformer model. This chapter examines the architecture of the encoder and decoder stacks, which are the fundamental building blocks.

You will learn to:

• Identify the components of a standard Transformer encoder layer, including multi-head self-attention and position-wise feed-forward networks.
• Analyze the structure of a decoder layer, focusing on masked self-attention, encoder-decoder cross-attention, and feed-forward sub-layers.
• Understand the function of masked self-attention in preventing information flow from future tokens during sequence generation.
• Grasp how cross-attention allows the decoder to focus on relevant parts of the input sequence via the encoder's output.
• Recognize the role and implementation of residual connections (often denoted as $Add$) and layer normalization ($Norm$) in facilitating information propagation and stabilizing training within deep networks.
• Appreciate how stacking multiple identical layers forms the deep encoder and decoder structures.
• Trace the data flow from the input embeddings through the encoder and decoder stacks to the final output probabilities.