Having examined the core attention mechanisms in the previous chapter, we now assemble these components to construct the full Transformer architecture. This chapter provides a detailed look at the model's structure, explaining how the encoder and decoder stacks work together in sequence-to-sequence tasks.

You will learn about:

• The overall encoder-decoder layout.
• Input processing, including token embeddings and the addition of positional information using positional encoding techniques like sine and cosine functions.
• The structure of an encoder layer, comprising multi-head self-attention and a position-wise feed-forward network.
• The structure of a decoder layer, which includes masked multi-head self-attention, encoder-decoder attention, and a feed-forward network.
• The role of residual connections and layer normalization (Add & Norm) in stabilizing the network and improving gradient flow.
• How the decoder output is transformed into final token probabilities via a linear layer and softmax function.
• A practical implementation section focusing on building a single encoder layer.

By the end of this chapter, you will understand how these distinct parts integrate to form the complete Transformer model and the rationale behind each component's design.