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
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.
- Complete Generative AI Course With Langchain and Huggingface by Krish Naik
- Introduction to Generative AI:
- Understand the fundamentals of Generative AI and its applications.
- Explore the differences between traditional AI models and generative models.
- Getting Started with Langchain:
- Learn the basics of Langchain and its role in AI development.
- Set up your development environment and tools.
- Huggingface Integration:
- Integrate Huggingface's state-of-the-art models into your Langchain projects.
- Customize and fine-tune Huggingface models for specific applications.
- 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.
- Deployment Strategies:
- Learn various deployment strategies for AI models.
- Deploy your models to cloud platforms and on-premise servers for scalability and reliability.
- RAG Pipelines:
- Develop Retrieval-Augmented Generation (RAG) pipelines to enhance AI performance.
- Combine generative models with retrieval systems for improved information access.
- Optimizing AI Models:
- Techniques for monitoring and optimizing deployed AI models.
- Best practices for maintaining and updating AI systems.
- End-to-End Projects:
- Hands-on projects that provide real-world experience.
- Build, deploy, and optimize AI applications from scratch.