Having examined the theoretical underpinnings of the Transformer architecture in previous chapters, this chapter focuses on translating that theory into functional code. We will construct the core components of the Transformer model step-by-step using a common deep learning framework.

You will learn to implement:

• Scaled Dot-Product Attention, the fundamental attention mechanism.
• Multi-Head Attention, combining multiple attention perspectives.
• Position-wise Feed-Forward Networks.
• The structure of Encoder and Decoder layers, including residual connections and layer normalization.
• The complete Transformer model by assembling these parts.

By the end of this chapter, you will have a clear, operational implementation of the Transformer, providing a solid foundation for understanding how these models function at a code level and preparing you for subsequent chapters on scaling and optimization. We will establish a basic project setup and proceed logically through each architectural element.