Udemy – LangGraph- Develop LLM powered agents with LangGraph 2024-5
Udemy – LangGraph- Develop LLM powered agents with LangGraph 2024-5 Downloadly IRSpace
LangGraph- Develop LLM powered agents with LangGraph, This comprehensive course is designed to teach you how to QUICKLY harness the power the LangGraph library for LLM agentic applications. This course will equip you with the skills and knowledge necessary to develop cutting-edge LLM Agents solutions for a diverse range of topics. Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python & LangChain. I will be using Pycharm IDE but you can use any editor you’d like since we only use basic feature of the IDE like debugging and running scripts . Throughout the course, you will work on hands-on exercises and real-world projects to reinforce your understanding of the concepts and techniques covered. By the end of the course, you will be proficient in using LangGraph to create powerful, efficient, and versatile LLM applications for a wide array of usages.
What you’ll learn
- Become proficient in LangGraph
- Implement Advanced Agents
- Have end to end working LangGraph based generative AI agents
- Understand how to navigate inside the LangGraph opensource codebase
Who this course is for
- Software Engineers that want to learn how to build Generative AI based applications with LangChain
- Backend Developers that want to learn how to build Generative AI based applications with LangChain
- Fullstack engineers that want to learn how to build Generative AI based applications with LangChain
Specificatoin of LangGraph- Develop LLM powered agents with LangGraph
- Publisher : Udemy
- Teacher : Eden Marco
- Language : English
- Level : Intermediate
- Number of Course : 19
- Duration : 1 hours and 42 minutes
Content on 2024-6

Requirements
- This is not a beginner course. Solid software engineering concepts are needed
- I assume students will be familiar software engineering subjects such as: LangChain, git, python, pipenv, environment variables, classes, testing and debugging
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1.05 GB
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