Part 1: The AI Agent Engine: From Text to Large Language Models
This part lays the foundation for understanding how modern AI agents process and generate language. It begins by exploring how raw text can be represented in numerical form suitable for deep learning models, introducing techniques such as word embeddings and basic neural architectures. The focus then shifts to the Transformer model and explains how attention mechanisms revolutionized natural language processing. Finally, it examines how large language models (LLMs) are built by scaling transformers, discussing training strategies, instruction tuning, fine-tuning, and the evolution toward models capable of general-purpose reasoning. Together, these chapters provide the technical and conceptual groundwork for building intelligent AI agents.
This part has the following chapters:
- Chapter 1, Analyzing Text Data with Deep Learning
- Chapter 2, The Transformer: The Model Behind the Modern AI Revolution
- Chapter 3, Exploring LLMs as a Powerful AI Engine
