AI 技术基础与实战

本课程深入剖析AI核心原理与工程架构，助力开发者从技术视角构建高性能AI功能并实现系统级架构优化。 * **掌握大模型核心原理与技术选型**，实现架构优化 * **精通提示词工程与 Agent 落地**，提升开发效率 * **构建 RAG 知识库系统**，解决私有数据问答需求 * **设计 AI 驱动的业务方案**，打通技术与商业闭环

8 sections · 36 lessons

Course outline

Language Models Basics

  1. Inside ChatGPT
  2. How Prompts Work
  3. Probability & Randomness
  4. Temperature

How Models Learn

  1. Training Data Scale
  2. Generalization
  3. Parameters
  4. Tuning the Model

Training in Practice

  1. The Training Process
  2. Developing Understanding
  3. Scale of Computation
  4. Hardware Reality

Making AI Helpful

  1. Pre-training Basics
  2. Supervised Fine-Tuning
  3. Reinforcement Learning
  4. Complete Training Pipeline
  5. GPU Technology
  6. The Parallelization Problem

Transformer Innovation

  1. Introduction to Transformers
  2. Words to Numbers
  3. Understanding Embeddings
  4. Introduction to Attention
  5. How Attention Works

Connecting the Pieces

  1. Feed-Forward Networks
  2. Multi-Layer Basics
  3. From Vectors to Predictions
  4. Complete Pipeline

AI Strengths & Limitations

  1. What LLMs Excel At
  2. Next to Master
  3. Core LLM Limitations
  4. Structural Constraints

Practical AI Use

  1. Prompt Engineering Fundamentals
  2. Advanced Prompting Techniques
  3. AI Literacy: Engaging & Creating
  4. AI Literacy: Managing & Designing
  5. Your AI-Augmented Future

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