1st Edition
DeepSeek in Action LLM Deployment, Fine-Tuning, and Application
Part I: Theoretical Foundations and Technical Architecture of Generative AI 1. Core Principles of Transformer and Attention Mechanisms 2 DeepSeek-V3 Core Architecture and its Training Techniques in Detail 3 Introduction to DeepSeek-V3 Model-Based Development Part II: Development and Application of Generative AI and Advanced Prompt Design 4. A First Look at the DeepSeek-V3 Big Model 5. DeepSeek Open Platform and API Development Details 6. Dialogue Generation, Code Completion, and Customized Model Development 7. Conversation Prefix Completion, FIM and JSON Output Development Details 8. Callback Functions and Contextual Disk Caching 9. The DeepSeek Prompt Library: Exploring More Possibilities for Prompts Part III: Integration of Practical Experience and Advanced Applications 10. Integration Practice 1: LLM-Based Chat Client Development 11. Integration Hands-On 2: AI Assisted Development 12. Integration Practice 3: Assisted Programming Plugin Development Based on VS Code
Biography
Jing Dai graduated from Tsinghua University with research expertise in data mining, natural language processing, and related fields. With over a decade of experience as a technical engineer at leading companies including IBM and VMware, she has developed strong technical capabilities and deep industry insight. In recent years, her work has focused on advanced technologies such as large-scale model training, NLP, and model optimization, with particular emphasis on Transformer architectures, attention mechanisms, and multi-task learning.
