Q3,Q4,Q5 - Class -12: Introduction to LangGraph. A Simple Graph to understand State, Node & Edges

Рет қаралды 2,346

12 күн бұрын

👋 Hey AI Enthusiasts! In this video, we introduce you to LangGraph, a simple yet powerful tool designed to help you understand the concepts of State, Node, and Edges in graph-based systems. Whether you're new to graph theory or working with state machines, this tutorial provides a clear explanation of how LangGraph simplifies complex processes. You'll learn how states represent different conditions, how nodes function within a graph, and how edges connect these elements to define transitions.
Perfect for developers, data scientists, and anyone interested in understanding how graphs can model real-world scenarios, LangGraph helps make concepts like state management and graph traversal easier to grasp. By the end of this video, you’ll have a strong foundation to start working with LangGraph in your projects.
🔍 Topics Covered:
Introduction to LangGraph
What are States, Nodes, and Edges?
Understanding state machines with LangGraph
How to build simple graph models
Practical examples of LangGraph in action
Subscribe for more tutorials on graph theory, state machines, and modern AI tools!
📚 *What Will You Learn? in this course*
● Custom GPTs and Multi AI Agent Systems: Learn to fine-tuning foundational AI models, and market them in GPT stores. Learn key principles of designing effective AI agents, and organising a team of AI agents to perform complex, multi-step tasks. Apply these concepts to automate common business processes.
● Develop AI Powered Microservices: Master Python, build APIs using FastAPI, SQLModel, Postgres, Kafka, Kong, and leverage cutting-edge GenAI APIs like OpenAI, and Open Source AI LLMs.
● Cloud Native Expertise: Design and deploy cloud-native applications using Docker, DevContainers, TestContainers, Kubernetes, Terraform, and GitHub Actions.
● Distributed System Design: Designing systems that run on multiple computers (or nodes) simultaneously, interacting and coordinating their actions by passing messages over a network.
● Designing AI Solutions using Design Thinking and Behaviour Driven Development (BDD): We will learn to leverage these methodologies to create AI solutions that are not only technically sound but also highly user-centric and aligned with real-world needs.
● Fine-Tuning Open-Source Large Language Models using PyTorch, and Fast AI: We will learn to fine-tuning of open-source Large Language Models (LLMs) like Meta LLaMA 3 using PyTorch and Fast AI, with a focus on cloud-native training and deployment. We will set up development environments, preprocess data, fine-tune models, and deploy them using cloud native platforms.
● Physical AI and Humanoid Robotics: We will learn to design, simulate, and deploy advanced humanoid robots capable of natural interactions.
🛠️ *Tools & Technologies Covered:*
- Poetry
- Python
- FastAPI
- Docker
- Kafka
- Kong
- Pytorch
- ROS2
📌 Useful Links:
- github.com/panaversity/learn-applied-generative-ai-fundamentals/tree/main/21_langchain_ecosystem/langgraph/course-notebooks/module-1
- www.piaic.org
📣 Stay Connected:
Don't forget to like this video 👍, subscribe 🔔, and share 💌 it with your friends and colleagues who are interested in Python development and data science!
🎓 Perfect for beginners and experienced developers looking to enhance their Python skills!
#generativeai #artificialintelligence #AI #LLM #machinelearning #technology #future #CPU #GPU #NPU #cloudcomputing #opensource
- **Connect with the Instructors**: Have questions? Want to connect? Find us on LinkedIn:
- [Zia Khan](www.linkedin.com/in/ziaukhan/)
- [Qasim Sir](www.linkedin.com/in/sirqasim/)
- **Join Our Community**: Become a part of our growing Facebook group and stay updated with all the latest content and discussions:
[Official Facebook Group](web. groups/207857240128729)