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This repository offers a hands-on guide to mastering Generative AI with Langchain and Huggingface. It covers key concepts, practical implementation, and deployment strategies to help AI enthusiasts, developers, and professionals build and optimize AI models efficiently

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Generative-AI-with-Langchain-and-Huggingface

This repository provides a structured, hands-on approach to mastering Generative AI with Langchain and Huggingface. It consolidates resources, notebooks, and projects to help you build, deploy, and optimize AI applications using state-of-the-art generative models

About This Repository

This repository serves as a comprehensive guide for anyone looking to understand, build, and deploy Generative AI applications. The materials included cover foundational and advanced Generative AI concepts, practical exercises, and real-world projects to reinforce each topic.

Courses Referenced

  1. Complete Generative AI Course With Langchain and Huggingface by Krish Naik

What You Will Learn:

  1. Introduction to Generative AI:
    • Understand the fundamentals of Generative AI and its applications.
    • Explore the differences between traditional AI models and generative models.
  2. Getting Started with Langchain:
    • Learn the basics of Langchain and its role in AI development.
    • Set up your development environment and tools.
  3. Huggingface Integration:
    • Integrate Huggingface's state-of-the-art models into your Langchain projects.
    • Customize and fine-tune Huggingface models for specific applications.
  4. Building Generative AI Applications:
    • Step-by-step tutorials on creating advanced generative AI applications.
    • Real-world projects such as chatbots, content generators, and data augmentation tools.
  5. Deployment Strategies:
    • Learn various deployment strategies for AI models.
    • Deploy your models to cloud platforms and on-premise servers for scalability and reliability.
  6. RAG Pipelines:
    • Develop Retrieval-Augmented Generation (RAG) pipelines to enhance AI performance.
    • Combine generative models with retrieval systems for improved information access.
  7. Optimizing AI Models:
    • Techniques for monitoring and optimizing deployed AI models.
    • Best practices for maintaining and updating AI systems.
  8. End-to-End Projects:
    • Hands-on projects that provide real-world experience.
    • Build, deploy, and optimize AI applications from scratch.

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This repository offers a hands-on guide to mastering Generative AI with Langchain and Huggingface. It covers key concepts, practical implementation, and deployment strategies to help AI enthusiasts, developers, and professionals build and optimize AI models efficiently

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