Udemy - LangGraph- Develop LLM Powered AI agents with LangGraph

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U P L O A D E R
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3.44 GB | 8min 24s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English


Files Included :
001 Introduction.mp4 (41.95 MB)
002 Prerequisites (Please Watch this before enrolling).mp4 (22.22 MB)
003 Discord.mp4 (15.96 MB)
004 What is LangGraph.mp4 (12.2 MB)
005 Why LangGraph and AI Agents Applications.mp4 (81.36 MB)
006 But wait What are Graphs.mp4 (20.1 MB)
007 Flow Engineering.mp4 (18.74 MB)
008 LangGraph Core Components.mp4 (46.81 MB)
009 What are we building.mp4 (4.42 MB)
010 Project Setup.mp4 (48.79 MB)
011 Creating the Reflector Chain and the Tweet Reviosr Chain.mp4 (23.68 MB)
012 Defining our LangGraph Graph.mp4 (165.11 MB)
013 LangSmith Tracing.mp4 (32.68 MB)
014 Personal Message.mp4 (13.1 MB)
015 What are we building.mp4 (158.89 MB)
016 Project Setup.mp4 (22.68 MB)
017 Actor Agent.mp4 (155.54 MB)
018 Revisor Agent.mp4 (31.91 MB)
019 Tool Executor Agent (Part A).mp4 (60.23 MB)
020 Tool Executor Agent (Part B).mp4 (122.34 MB)
021 Tool Executor Agent (Part C).mp4 (31.8 MB)
022 Building our LangGraph Graph.mp4 (72.27 MB)
023 What are we building ReAct AgentExecutor in LangGraph.mp4 (8.67 MB)
024 Setup.mp4 (62.05 MB)
025 ReAct Runnable.mp4 (31.68 MB)
026 AgentState.mp4 (53.12 MB)
027 Nodes.mp4 (18.55 MB)
028 Graph.mp4 (122.44 MB)
029 What are Building In this Section- Advanced RAG Architecture.mp4 (30.22 MB)
030 Corrective RAG flow.mp4 (16.35 MB)
031 Setup.mp4 (42.62 MB)
032 Code Structure.mp4 (38.72 MB)
033 Ingestion.mp4 (76.96 MB)
034 State.mp4 (11.02 MB)
035 Retriever Node.mp4 (19.83 MB)
036 Document Grader Node.mp4 (83.55 MB)
037 Tavily Web Search Node.mp4 (33.19 MB)
038 Augmentation Generation Node.mp4 (87.46 MB)
039 Building & Running Graph.mp4 (39 MB)
040 Self RAG Intro.mp4 (17.44 MB)
041 Self RAG- Implementation.mp4 (145.3 MB)
042 Adaptive RAG- Implementation.mp4 (165.94 MB)
043 Persistence in LangGraph.mp4 (25.22 MB)
044 MemorySaver + Interrupts = Human In The Loop.mp4 (31.73 MB)
045 MemorySaver.mp4 (49.11 MB)
046 SqliteSaver.mp4 (86.74 MB)
047 Implementing Async Execution.mp4 (59.86 MB)
048 Parallel node fan-out and fan-in with extra steps.mp4 (20.02 MB)
049 Conditional Branching With Async Execution.mp4 (60.65 MB)
050 Async Nodes Benefits VS Challenges.mp4 (24.64 MB)
051 Intro.mp4 (32.3 MB)
052 LangGraph Studio (AKA LangGraph IDE).mp4 (70.59 MB)
053 LangGraph Cloud API - Environment Setup (Local).mp4 (23.02 MB)
054 LangGraph Cloud API Deep Dive - Assistants, Threads, Runs (Local).mp4 (120.78 MB)
055 Deploying to LangGraph Cloud.mp4 (68.01 MB)
056 Agents In Production.mp4 (60.65 MB)
057 LangGraph VS CrewAI main difference.mp4 (39.39 MB)
058 GPT Researcher - Setup.mp4 (26.15 MB)
059 GPT Researcher.mp4 (188.66 MB)
060 GPT Researcher - Architecture.mp4 (32.27 MB)
061 Multi Agent Architecture.mp4 (44.04 MB)
062 Generative UI.mp4 (71.19 MB)
063 Have a technical issue WATCH THIS FIRST I Promise this will help!.mp4 (42.33 MB)
064 Bonus.mp4 (40.67 MB)
]
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Langgraph- Develop Llm Powered Agents With Langgraph
Last updated 9/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 2.00 GB | Duration: 6h 12m​

Learn LangGraph by building FAST a real world generative ai LLM Agents (Python)

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
LangGraph Ecosystem: LangGraph Studio/ IDE, LangGraph Cloud API, LangGraph Cloud Managed Service

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

Description
Welcome to first LangGraph Udemy course - Unleashing the Power of LLM Agents!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 .The topics covered in this course include:LangChainLCELLangGraphAgentsMulti AgentsReflection AgentsReflexion AgentsLangSmithCrewAI VS LangGraphAdvanced RAGCorrective RAGSelf RAgAdaptive RAGGPT ResearcherLangGraph Ecosystem:LangGraph Studio / LangGraph IDELangGraph Cloud API LangGraph Cloud Managed ServiceThroughout 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.This is not just a course, it's also a community. Along with lifetime access to the course, you'll get:Dedicated troubleshooting support with meGithub links with additional AI resources, FAQ, troubleshooting guidesNo extra cost for continuous updates and improvements to the courseDISCLAIMERSPlease 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. 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.

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

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