祝融说。

FORM NOT VOID, MIND NO CORE

AI Engineer in Action: From Python Fundamentals to LLM Applications and Performance Optimization rc
Grow from Python fundamentals to building, optimizing, and deploying real LLM applications as a full-stack AI engineer.
Visual Guide 5 min
Introduction
Introduction: Embracing the AGI Era, Becoming an AI Engineer Who Shapes the Future
08-10
Part One: Foundational Skills -- A Lofty Tower Rises from the Ground
Chapter 1: Python Programming Essentials for AI Engineers
08-10
Chapter 2: Data Science Core Toolchain in Practice
08-10
Chapter 3: Building the Backend Foundation for AI Services
08-10
Chapter 4: An Engineer's Required Course: Linux, Docker, and Performance Monitoring
08-10
Part Two: Core Capabilities -- Deep Dive into the Heart of Large Language Models
Chapter 5: The Door to Deep Learning: From Neural Networks to PyTorch in Practice
08-10
Chapter 6: The Core of Natural Language Processing: From Word Embeddings to the Transformer
08-10
Chapter 7: The Era of Large Language Models: Full Technical Landscape of LLMs
08-10
Chapter 8: Customizing Your Own LLM: Efficient Fine-Tuning in Practice
08-10
Chapter 9: Unleashing the Potential of LLMs: Building RAG and Agents
08-10
Part Three: Advanced Progress -- The Necessary Path from Good to Great
Chapter 10: Extreme Performance: Optimizing LLM Inference Services
08-10
Chapter 11: Large-Scale Deployment: GPU Cluster Management and Virtualization
08-10
Chapter 12: When LLMs Meet Knowledge Graphs: Building and Applications
08-10
Chapter 13: Toward Excellence: Career Development for AI Engineers and Future Outlook
08-10
Appendix
Appendix
08-10