Название: ChatGPT: Principles and Architecture
Автор: Ge Cheng
Издательство: Elsevier
Год: 2025
Страниц: 280
Язык: английский
Формат: epub (true)
Размер: 16.9 MB

ChatGPT: Principles and Architecture bridges the knowledge gap between theoretical AI concepts and their practical applications. It equips industry professionals and researchers with a deeper understanding of Large Language Models (LLMs), enabling them to effectively leverage these technologies in their respective fields. In addition, it tackles the complexity of understanding Large Language Models and their practical applications by demystifying underlying technologies and strategies used in developing ChatGPT and similar models. By combining theoretical knowledge with real-world examples, the book enables readers to grasp the nuances of AI technologies, thus paving the way for innovative applications and solutions in their professional domains.

Sections focus on the principles, architecture, pretraining, transfer learning, and middleware programming techniques of ChatGPT, providing a useful resource for the research and academic communities. It is ideal for the needs of industry professionals, researchers, and students in the field of AI and Computer Science who face daily challenges in understanding and implementing complex Large Language Model technologies.

Offers comprehensive insights into the principles and architecture of ChatGPT, helping readers understand the intricacies of Large Language Models
Details Large Language Model technologies, covering key aspects such as pretraining, transfer learning, middleware programming, and addressing technical aspects in an accessible manner
Includes real-world examples and case studies, illustrating how Large Language Models can be applied in various industries and professional settings
Provides future developments and potential innovations in the field of Large Language Models, preparing readers for upcoming changes and technological advancements

Main Content of the Book:
This book is designed to help readers deeply understand ChatGPT and its related technologies. It consists of 11 chapters that comprehensively explore various aspects.
Chapter 1 provides an in-depth analysis of the technological evolution of large language models, supporting technologies, and technology stacks, and discusses their significant impact on society.
Chapter 2 elaborates on the theoretical foundations and main components of the Transformer model, revealing the principles and applications behind these technologies.
Chapter 3 delves into the generative pretraining process and principles of GPT.
Chapter 4 primarily explores technologies such as layer normalization, orthogonal initialization, and reversible tokenization in GPT-2, and provides a detailed analysis of GPT-2 autoregressive generation process.
Chapter 5 introduces GPT-3 sparse attention mechanisms, metalearning, and content-based learning concepts, and discusses the application of Bayesian inference in conceptual distributions.
Chapter 6 details the pretraining datasets and data processing methods for large language models, as well as distributed training models and architectures.
Chapter 7 deeply analyzes the fundamental principles of the proximal policy optimization (PPO) algorithm.
Chapter 8 focuses on the fine-tuning datasets of reinforcement learning with human feedback (RLHF) and the application of PPO in InstructGPT, discussing the capabilities of multiturn dialog and the necessity of human feedback reinforcement learning.
Chapter 9 explores how to transfer large language models to specific domains at low resource costs.
Chapter 10 primarily introduces the middleware technologies involved in the development of large language models.
Chapter 11 predicts and prospects the future development trends of large language models.

Target Audience for This Book:

- Product managers in the AI field: For product managers looking to incorporate AI features into their products, understanding the basic principles and operational mechanisms of large language models like ChatGPT is crucial. From this book, they can learn about the design philosophies and construction methods of large language models, as well as how to integrate these models into their products. They can also better understand the performance bottlenecks of their products, which aids in more precise product planning.
- Researchers in AI-related fields: For AI researchers, this book can serve as a textbook for a deep understanding of large language models. Whether it's the details of the Transformer model or tips on training and optimizing GPT models, this book provides thorough explanations. More importantly, this book explores some of the cutting-edge research areas, such as human feedback reinforcement learning and bootstrap labeling algorithms.
- Engineers specializing in large-scale data processing and analysis: For engineers facing challenges such as efficiently processing large-scale data or building distributed training architectures, this book offers many valuable suggestions and ideas. For example, Chapter 6 delves deeply into data processing and distributed training patterns.
-  AI enthusiasts and technologically savvy individuals in everyday life: If you are an AI technology enthusiast or someone who uses technology to improve everyday life, this book is also suitable for you. The introduction to Large Language Models in this book is easy to understand, providing a comprehensive overview of this powerful technology. More interestingly, this book offers many practical usage tips and case studies that can be directly applied to your life or work.