Must-Know 2024 AI Agent (Application Walkthrough + Project Practice), One-Stop Agent Application

Plain-language explanation of the Agent core architecture

The Agent core architecture is a design blueprint for intelligent systems that helps us complete various tasks. This course reveals the mysteries of the Agent core architecture in accessible language, letting you easily understand and apply it to real scenarios.

Full workflow analysis of classic frameworks

Full workflow analysis of classic frameworks step-by-step breaks down several classic technical frameworks, from whole to detail, helping students comprehensively understand these frameworks. We'll deep-dive into how they're built, so you clearly understand how they work.

Source-code walkthrough of mainstream projects

By reading the source code of mainstream projects, we can better understand their design thinking and implementation details. In this course, we'll walk you through mainstream project source code, learning best practices and techniques in project development.

One-stop Agent application

This course aims to provide students with a one-stop Agent application solution, letting you easily get started and apply Agent technology to real projects. Through this course, you'll become an Agent application expert!

Curriculum

1. Agent Task Solving & Architecture Analysis

An in-depth introduction to the various tasks Agents can solve, analyzing the architecture principles behind them, giving you a comprehensive understanding of Agents.

2. The Role of the RAG Retrieval Module & Application Examples

Explore the role of the RAG retrieval module, showing its application scenarios in different domains through real cases.

3. MOE Mixture-of-Experts System & Multi-Agent Examples

Introduce the concepts of MOE and multi-agent systems, demonstrating their application through examples.

4. Mainstream Agent Framework Application Practice

Through practical projects, learn how mainstream Agent frameworks are applied, so you can flexibly use them to solve real problems.

5. MetaGpt Framework Walkthrough & Application Practice

Deeply interpret the MetaGpt framework and demonstrate its application in NLP through practical projects.

6. Stanford AI Town Multi-Agent Application Practice

Through the Stanford AI Town case, understand multi-agent system application scenarios and practical techniques.

7. RAG Optimization Solutions & Fine-Tuning Strategies

Introduce RAG optimization solutions and fine-tuning strategies to help you improve Agent system performance.

8. LLM & Downstream Application Task Examples

Through LLM and downstream application task examples, understand this technology's application scenarios in real projects.

9. LORA Fine-Tuning of Large Models Practice

Deeply explore LORA fine-tuning methods for large models and demonstrate application techniques through practical projects.