Introduction to Transformer Architecture

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

Throughout this introductory chapter, we will break down the core concepts that define the Transformer architecture. We will start by examining the historical context and motivation behind its development, highlighting how it addresses limitations found in previous models. This will set the stage for a detailed breakdown of the Transformer's important 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 look into the self-attention mechanism, exploring 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 get into the details of more advanced applications and variants of the Transformer in subsequent chapters.