Introduction to Transformer Architecture

In this chapter, we delve into the Transformer architecture, a groundbreaking innovation in the realms of natural language processing (NLP) and artificial intelligence (AI). As the foundational component driving numerous state-of-the-art models, grasping the Transformer is crucial for anyone engaged in advancing machine learning technologies.

Throughout this introductory chapter, we will unravel the core concepts that define the Transformer architecture. We will commence by examining the historical context and motivation behind its development, highlighting how it addresses limitations found in previous models. This will pave the way for a detailed breakdown of the Transformer's key components, such as self-attention mechanisms and feed-forward neural networks.

You will learn how these components collaborate to process and transform data efficiently. We will also introduce the mathematical foundations that underpin these mechanisms, providing you with a solid grounding in both theory and application. For instance, we'll explore the self-attention mechanism, delving into how it computes attention scores to weigh the importance of different input tokens, a process essential for language comprehension.

By the chapter's conclusion, you will possess a comprehensive understanding of the Transformer model's basic structure and functionality. This knowledge will equip you with the necessary insights to delve deeper into the complexities of more advanced applications and variants of the Transformer in subsequent chapters.