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Large Language Models LLM for Everyone Part 5 - Building Your Own AI Agent with LangGraph

AI Agent: A total integration of the latest AI technology! Implement various AI agents and learn to build your own AI agent using LangGraph.

(4.9) 33 reviews

338 learners

  • AISchool
ai활용
에이전트
LangGraph
AI Agent
LangChain
RAG
openAI API

Reviews from Early Learners

What you will gain after the course

  • How to Implement AI Agents Using LangGraph

  • Concept and Use Cases of AI Agents

  • Various AI Agent Architectures

  • Building my own AI Agent with LangGraph

  • How to build an advanced RAG system with LangGraph

AI Agent, a culmination of the latest AI technologies!
By implementing various AI agents, you will learn how to implement your own AI agent using LangGraph.

By creating various AI agents with LangGraph, you will gradually learn the components and various architectures required to implement AI agents.

  • Learn how to use the LangGraph library.

  • Learn how to implement your own AI agent using LangGraph.

Who is this course for?

Anyone who wants to create their own AI agent with LangGraph

Anyone who wants to learn various AI agent architectures to build a deep RAG system

Anyone who wants to develop a service using the latest LLM model

Player Course

👋 This course requires prior knowledge of Python, Natural Language Processing (NLP), LLM, and LangChain. Please take the following courses first, or acquire equivalent knowledge before taking this course.

Large Language Model for Everyone Part 2 - Building Your Own ChatGPT with LangChain

Q&A 💬

Q. What is an AI agent?

An AI agent is a software program that operates autonomously within a specific environment and performs tasks to achieve a given goal. This agent perceives its surroundings , makes decisions based on those decisions, takes actions , evaluates the results, learns, and evolves to make better decisions. An AI agent primarily consists of the following core components.


1. Environment

This refers to the external world with which the agent interacts. This can be a physical environment or a virtual environment within a software system. AI agents collect data from this environment and make decisions based on that data.


2. Sensors

AI agents gather information from their environment through sensors. For physical robots, these sensors can be hardware like cameras or microphones, while for software agents, they can gather information from APIs or databases.


3. Actuators

An agent is a tool or method used to influence its environment. For example, a robot can control mechanical devices like arms or wheels to take physical actions, while a software agent can execute code or manipulate data to produce results.


4. Goals

AI agents typically have one or more goals. These goals guide the agent to complete a specific task or reach a specific state in the environment. These goals can be explicitly stated or learned through techniques like reinforcement learning.


5. Action & Decision Making

AI agents analyze information received from the environment and make optimal decisions among possible actions to achieve a given goal. This can be a rule-based system or a complex algorithm such as reinforcement learning or deep neural networks.


6. Learning

Through learning, AI agents improve their performance over time. A prime example is using machine learning techniques to learn from past experiences to make better decisions. This allows the agent to quickly adapt to changes in the environment and improve its behavioral strategies.


Q. Is player knowledge required?

This lecture [Large Language Model for Everyone LLM Part 5 - Building Your Own AI Agent with LangGraph] covers how to build 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, and LangChain. Therefore, if you lack prior knowledge, we recommend taking the preceding lecture [ Large Language Model for Everyone LLM (Large Language Model) Part 2 - Building Your Own ChatGPT with LangChain] first.

Recommended for
these people

Who is this course right for?

  • Deep Learning Research Job Aspirants

  • Person wishing to pursue AI/Deep Learning research

  • Those preparing for AI graduate school

  • Want to build your own AI agent with LangGraph.

  • For those wanting to build an advanced RAG system using LangGraph, beyond basic ones.

Need to know before starting?

  • Python experience

  • Pre-course [Large Language Model LLM(Large Language Model) for Everyone Part 2 - Creating My Own ChatGPT with LangChain] Course Experience

Hello
This is

9,334

Learners

706

Reviews

353

Answers

4.6

Rating

30

Courses

Curriculum

All

73 lectures ∙ (19hr 26min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

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33 reviews

4.9

33 reviews

  • aibot님의 프로필 이미지
    aibot

    Reviews 1

    Average Rating 5.0

    5

    38% enrolled

    inflearn, fastcampus등 다양한 플래폼의 ai 강의를 듣는데 제일 만족스럽습니다. 회사의 프로젝트와 연관도도 제일 높습니다.

    • YCorn님의 프로필 이미지
      YCorn

      Reviews 1

      Average Rating 4.0

      4

      39% enrolled

      다른 것은 좋은 데요。 발표에 사용했던 powerpoint 자료도 upload하면 좋을 것 같아요。 notebook자료 말고요。

      • 빛나는봄님의 프로필 이미지
        빛나는봄

        Reviews 1

        Average Rating 5.0

        5

        98% enrolled

        LangGraph를 활용한 AI 에이전트 구축 심화 학습에 실질적인 도움을 주는, 최신 기술과 다양한 활용 사례를 배울 수 있는 유익한 강의였습니다.

        • Alex님의 프로필 이미지
          Alex

          Reviews 7

          Average Rating 5.0

          5

          5% enrolled

          이처럼 LLM을 배우는데 있어서 짜임새 있는 교육을 받을 수 있는 수강이 몇 없다고 생각합니다. 어려운 개념인 만큼 습득하는 지식의 순서가 중요하다고 생각하는데 이 강의뿐만 아니라 예제부터 배우는 자연어처리 수업도 모두 퀄리티가 좋으니 이 글을 보시는 분들께 적극적으로 추천드리고 싶습니다.

          • Chulgil Lee님의 프로필 이미지
            Chulgil Lee

            Reviews 1

            Average Rating 5.0

            5

            7% enrolled

            강의 슬라이드와 실습용 코랩 노트북 덕분에 흐름을 따라가기 쉬웠습니다. 필요한 내용만 콕 집어 설명해주셔서 이해가 잘 되었어요. 군더더기 없이 핵심에 집중한 강의 스타일이 마음에 들었습니다. 실습 중심의 구성이라 배운 내용을 바로 적용해볼 수 있어 좋았습니다. 앞으로도 이런 구조의 강의가 많아졌으면 좋겠습니다!

            $59.40

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