강의

멘토링

로드맵

BEST
AI Development

/

AI Agent Development

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) 30 reviews

310 learners

  • AISchool
ai활용
에이전트
LangGraph
AI Agent
LangChain
RAG
openAI API

Reviews from Early Learners

What you will learn!

  • 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

9,054

Learners

662

Reviews

350

Answers

4.6

Rating

29

Courses

Curriculum

All

67 lectures ∙ (17hr 56min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

30 reviews

4.9

30 reviews

  • songhunhan968882님의 프로필 이미지
    songhunhan968882

    Reviews 1

    Average Rating 5.0

    5

    38% enrolled

    Tôi đã nghe các bài giảng AI trên nhiều nền tảng khác nhau như inflearn, fastcampus, v.v. và tôi hài lòng nhất với khóa học này. Nó cũng liên quan nhất đến các dự án của công ty.

    • hsc650031님의 프로필 이미지
      hsc650031

      Reviews 1

      Average Rating 4.0

      4

      39% enrolled

      Một điều nữa là tôi nghĩ sẽ tốt hơn nếu sử dụng các tài liệu powerpoint. Không phải các tài liệu notebook.

      • kwangchoon9989님의 프로필 이미지
        kwangchoon9989

        Reviews 1

        Average Rating 5.0

        5

        98% enrolled

        Đây là một bài giảng hữu ích, cung cấp kiến thức chuyên sâu về xây dựng AI Agent bằng LangGraph, giúp ích thiết thực, đồng thời học được các công nghệ mới nhất và nhiều trường hợp ứng dụng đa dạng.

        • canflight0080096님의 프로필 이미지
          canflight0080096

          Reviews 7

          Average Rating 5.0

          5

          5% enrolled

          Tôi nghĩ rằng không có nhiều khóa học được xây dựng bài bản để học về LLM như thế này. Vì đây là một khái niệm khó, tôi nghĩ rằng thứ tự kiến thức thu được là rất quan trọng, và không chỉ bài giảng này mà cả các lớp học xử lý ngôn ngữ tự nhiên bắt đầu từ các ví dụ đều có chất lượng tốt, vì vậy tôi muốn tích cực giới thiệu chúng cho những người đang đọc bài viết này.

          • codenavi님의 프로필 이미지
            codenavi

            Reviews 1

            Average Rating 5.0

            5

            7% enrolled

            Nhờ slide bài giảng và notebook Colab thực hành mà tôi dễ dàng theo dõi được tiến trình. Thầy/cô đã giải thích đúng trọng tâm những nội dung cần thiết nên tôi hiểu bài rất rõ. Tôi thích phong cách giảng dạy tập trung vào trọng tâm, không lan man. Tôi thấy rất tốt vì cấu trúc bài giảng tập trung vào thực hành, giúp tôi có thể áp dụng ngay những kiến thức đã học. Tôi hy vọng trong tương lai sẽ có nhiều bài giảng với cấu trúc như thế này!

            Limited time deal ends in 8 days

            $1,225,662.00

            24%

            $59.40

            AISchool's other courses

            Check out other courses by the instructor!

            Similar courses

            Explore other courses in the same field!