
모두를 위한 대규모 언어 모델 LLM Part 5 - LangGraph로 나만의 AI 에이전트 만들기
AISchool
최신 AI 기술의 총집합체인 AI 에이전트! 다양한 AI 에이전트들을 구현해보면서 LangGraph를 이용한 나만의 AI 에이전트 구현법을 학습해봅니다.
중급이상
LangGraph, AI Agent, LangChain
I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.
How to Implement an AI Agent with LangGraph
How to Implement Various Practical AI Agents
Practical Use Cases of AI Agents
Various AI Agent Architectures
AI Agent, the Megatrend in the Tech Industry!
Learn how to implement practical AI agents through a variety of projects!
We will learn step-by-step how to create AI agents using LangGraph while creating various practical AI agents.
Anyone who wants to create a practical AI agent
Anyone who wants to create their own AI agent using LangGraph
Anyone who wants to improve their LangGraph implementation skills
Anyone who wants to develop a service using the latest LLM model
👋 This course requires prior knowledge of Python, Natural Language Processing (NLP), LLM, LangChain, and LangGraph . Be sure to take the courses below first or have equivalent knowledge before taking this course.
Q. What are the benefits of learning how to implement AI agents using LangGraph through projects?
LangGraph is a powerful framework that allows for the flexible construction of complex AI agents , and has recently attracted attention as a key tool for AI agent development.
Learning LangGraph on a project-by-project basis has the following advantages:
1. Practice-oriented learning :
Rather than simply learning theories, you can gain practical experience by creating AI agents that actually work. You can build capabilities that can be applied directly to the field.
2. Experience in designing complex agent logic :
LangGraph allows you to visually and clearly structure complex logic, such as multi-step inference, branching, and stateful flows. This will help you develop the ability to design and implement advanced agents.
3. Expanding understanding of the LangChain ecosystem :
Since LangGraph operates based on LangChain, you can naturally learn the core concepts of LangChain and how to utilize various tools.
4. Acquire the latest technology trends :
AI agents are a core technology that will be applied to various services in the future. LangGraph is a tool that is rapidly spreading in this flow, and learning it in advance can increase your competitiveness.
5. Can be used as a portfolio :
The results created through the project can be used as your own portfolio, becoming a powerful weapon when seeking employment or changing careers.
Q. Is player knowledge required?
This lecture [ Large-Scale Language Model for Everyone LLM Part 6 - Implementing AI Agents Using LangGraph through Projects ] covers a project practice of implementing AI agents using the LangGraph library and LLM . Therefore, the lecture proceeds under the assumption that you have basic knowledge of Python, natural language processing, LLM, LangChain, and LangGraph. Therefore, if you lack prior knowledge, please be sure to take the preceding lecture [ Large-Scale Language Model for Everyone LLM Part 5 - Build Your Own AI Agent with LangGraph ] first.
Who is this course right for?
For those who want to create their own AI agent using LangGraph
For those who want to find a job in deep learning research.
Anyone interested in conducting research related to AI/deep learning
Someone preparing for AI graduate school
Anyone who wants to implement practical AI agents
Need to know before starting?
Experience with Python
Course Review: [Large Language Models (LLM) for Everyone Part 5 - Building Your Own AI Agent with LangGraph]
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선수강의인 LangGraph 강의를 수강했다면 정말 가볍게 볼 수 있는 내용입니다. 선수 강좌에서는 논문을 참고하며 다양한 아키텍처를 구현하면서 학습이 잘 됐는데, 오히려 현 강의에서는 간단한 그래프 위주의 내용이어서 김이 조금 샌 부분이 있습니다. 현 강의는 시중에서 제공되는 다양한 AI서비스를 따라서 구현하는 클론 프로젝트 중심인데, 그만큼 시중에 있는 AI 서비스가 생각보다 간단한 것이라고 생각해도 될 것 같네요. 공부가 목적이라면 선수 강의를 더 추천하고, 실무에 쉽고 효율적으로 바로 써먹는 것이 목적이라면 현 강의가 더 좋아보이긴 합니다! 그리고 강의를 진행하시면서 결과를 단순히 확인하거나 비교만 하는 과정에서 쭉 읽어나가시기만 하는 부분이 꽤 많았는데, 학습하는 입장에서는 비효율적으로 느껴졌습니다. 그래도 그런 부분은 알아서 스킵하면서 필요한 부분 잘 참고하면서 수강 했습니다. 좋은 내용 감사해요!
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