ChatGPT

Principles and Architecture

Ge Cheng

• Publisher: Elsevier
• Year: 2025
• Topic: Computer Science
• eBook ISBN:
  9780443274374
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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, 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

Table of contents

• Title of Book
• Chapter 1. A new milestone in artificial intelligence—ChatGPT
• Chapter 2. In-depth understanding of the transformer model
• Chapter 3. Generative pretraining
• Chapter 4. Unsupervised multitask and zero-shot learning
• Chapter 5. Sparse attention and content-based learning
• Chapter 6. Pretraining strategies for large language models
• Chapter 7. Proximal policy optimization
• Chapter 8. Human feedback reinforcement learning
• Chapter 9. Low-resource domain transfer of large language models
• Chapter 10. Middleware
• Chapter 11. The future path of large language models
• Index