Ge Cheng | ChatGPT: Principles and Architecture (2025) [PDF, EPUB, MOBI] -Автор: Ge Cheng Издательство: Elsevier ISBN: 978-0-443-27436-7 Жанр: Искусственный интеллект. Нейронные сети Язык: Английский Формат: PDF, EPUB, MOBI Качество: Изначально электронное (ebook) Иллюстрации: Цветные и черно-белые Описание: 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
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Table of Contents Cover image Title page Copyright Preface Main Content of the Book Target Audience for This Book Contact the Author Acknowledgments Chapter 1. A new milestone in artificial intelligence—ChatGPT Abstract 1.1 The development history of ChatGPT 1.2 The capability level of ChatGPT 1.3 The technical evolution of large language models 1.4 The technology stack of large language model 1.5 The impact of large language models 1.6 The challenges of training or deploying large models 1.7 The limitations of large language models 1.8 Summary Chapter 2. In-depth understanding of the transformer model Abstract 2.1 Introduction to the transformer model 2.2 Self-attention mechanism 2.3 Multihead attention mechanism 2.4 Feedforward neural network 2.5 Residual connection 2.6 Layer normalization 2.7 Position encoding 2.8 Training and optimization 2.9 Summary Chapter 3. Generative pretraining Abstract 3.1 Introduction to generative pretraining 3.2 Generative pretraining model 3.3 The generative pretraining process 3.4 Supervised fine-tuning 3.5 Summary Chapter 4. Unsupervised multitask and zero-shot learning Abstract 4.1 Encoder and decoder 4.2 GPT-2 4.3 Unsupervised multitask learning 4.4 The relationship between multitask and zero-shot learning 4.5 The autoregressive generation process of GPT-2 4.6 Summary Chapter 5. Sparse attention and content-based learning Abstract 5.1 GPT-3 5.2 The sparse transformer 5.3 Meta-learning and in-context learning 5.4 Bayesian inference of concept distributions 5.5 Thought chains 5.6 Summary Chapter 6. Pretraining strategies for large language models Abstract 6.1 Pre-training datasets 6.2 Processing of pretraining data 6.3 Distributed training patterns 6.4 Technical approaches to distributed training 6.5 Examples of training strategies 6.6 Summary Chapter 7. Proximal policy optimization Abstract 7.1 Traditional policy gradient methods 7.2 Actor-Critic 7.3 Trust region policy optimization 7.4 Principles of the proximal policy optimization algorithm 7.5 Summary Chapter 8. Human feedback reinforcement learning Abstract 8.1 Reinforcement learning in ChatGPT 8.2 InstructGPT training dataset 8.3 Training stages of human feedback reinforcement learning 8.4 Reward modeling algorithms 8.5 PPO in InstructGPT 8.6 Multiturn dialogue capability 8.7 The necessity of human feedback reinforcement learning 8.8 Summary Chapter 9. Low-resource domain transfer of large language models Abstract 9.1 Self-instruct 9.2 Constitutional artificial intelligence 9.3 Low-rank adaptation 9.4 Quantization 9.5 SparseGPT 9.6 Case studies 9.7 Summary Chapter 10. Middleware Abstract 10.1 LangChain 10.2 AutoGPT 10.3 Competitors in middleware frameworks 10.4 Summary Chapter 11. The future path of large language models Abstract 11.1 The path to strong artificial intelligence 11.2 Data resource depletion 11.3 Limitations of autoregressive models 11.4 Embodied intelligence 11.5 Summary
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