
gen-ai-experiments
Collection of Jupyter notebooks is designed to provide you with a comprehensive guide to various AI tools and technologies
Stars: 69

Gen-AI-Experiments is a structured collection of Jupyter notebooks and AI experiments designed to guide users through various AI tools, frameworks, and models. It offers valuable resources for both beginners and experienced practitioners, covering topics such as AI agents, model testing, RAG systems, real-world applications, and open-source tools. The repository includes folders with curated libraries, AI agents, experiments, LLM testing, open-source libraries, RAG experiments, and educhain experiments, each focusing on different aspects of AI development and application.
README:
Dive into practical Generative AI (GenAI) experiments and master the latest Large Language Models (LLMs), AI Agents, and open-source tools. This repository is your ultimate resource for learning by doing!
Welcome to Gen-AI-Experiments! This repository is meticulously crafted to be your go-to resource for hands-on learning and experimentation in the exciting field of Generative AI. Whether you're a beginner exploring AI or an experienced practitioner, you'll find valuable notebooks and experiments to level up your skills.
Why Star This Repo?
- Learn by Doing: Dive into real-world examples and practical Jupyter notebooks that you can run and modify.
- Master Cutting-Edge Tech: Explore AI Agents, Retrieval-Augmented Generation (RAG), LLM testing, and much more.
- Unlock 100+ Open-Source Libraries: Discover and utilize a curated collection of essential AI libraries, from LangChain to Weaviate.
- Stay Ahead of the Curve: Keep up with the rapidly evolving world of Generative AI with tested and working examples.
- Boost Your Portfolio: Use these experiments to build your own impressive AI projects and showcase your expertise.
This repository is thoughtfully organized to guide you through different facets of GenAI:
Folder Name | Description | Key Takeaway |
---|---|---|
100-OS-Libraries/ |
Curated collection of 100+ essential open-source libraries with practical examples. | Master essential AI tools: Langchain, FAISS, Streamlit, and more. |
AI-agents/ |
Notebooks and scripts for building intelligent AI agents for various use cases. | Build autonomous agents for tasks like web browsing, JEE prep, and travel assistance. |
Experiments/ |
Exploratory notebooks experimenting with innovative AI workflows and use cases. | Discover creative AI applications in image processing, web scraping, and more. |
LLM-Testing/ |
Testing and benchmarking notebooks for Large Language Models (LLMs) like GPT, Llama, and others. | Evaluate and compare the performance of different state-of-the-art LLMs. |
Open-Source-Libraries/ |
In-depth demos of key open-source AI libraries and frameworks. | Deep dive into advanced usage of libraries like LangGraph and Guardrails. |
RAG-Experiments/ |
Focuses on Retrieval-Augmented Generation (RAG) systems and their practical applications. | Implement and optimize RAG pipelines for enhanced AI performance. |
educhain-experiments/ |
Experiments and use cases for AI in education and blockchain integration (EduChain). | Explore AI's potential in education and innovative integrations. |
Lectures/ |
Lecture materials and notebooks for in-depth learning. | Access structured learning content on various AI topics. |
- AI Agents: Build intelligent, autonomous systems for specific tasks using cutting-edge frameworks.
- LLM Testing: Learn robust methods to evaluate and benchmark the performance of Large Language Models.
- RAG Systems: Implement and optimize Retrieval-Augmented Generation for knowledge-enhanced AI applications.
- Real-World Applications: Explore practical AI use cases in education, automation, data analysis, and more.
- Open-Source Tools: Gain hands-on experience with popular open-source libraries and frameworks that power the GenAI revolution.
-
Programming Language:
Python
- the leading language for AI development. -
Interactive Learning:
Jupyter Notebooks
- for interactive coding and experimentation. -
Powerful LLMs:
GPT
,Llama
,Nemotron
, and more - experiment with the latest models. -
Data Visualization:
Matplotlib
,Seaborn
- for insightful data analysis. -
Essential Libraries:
Hugging Face Transformers
,LangChain
,PyTorch
,TensorFlow
, and 100+ more!
-
Clone the Repository:
git clone https://github.com/buildfastwithai/gen-ai-experiments.git cd gen-ai-experiments
-
Install Dependencies:
pip install -r requirements.txt
- Explore & Experiment! Navigate to the folders and run the Jupyter Notebooks to begin your GenAI journey.
⭐️ Star this repo if you find it helpful! Your support fuels more valuable resources and helps others discover this learning hub.
We welcome contributions from the community!
- Report Bugs: Help us improve by opening issues for any bugs or problems you encounter.
- Suggest Features: Share your innovative ideas and feature requests.
- Submit Pull Requests: Contribute your own experiments, notebooks, or improvements.
- Share Feedback: Let us know how we can make this repository even better for the GenAI community!
This repository is released under the MIT License. Feel free to use, modify, and share for your projects. Just remember to give credit where it's due!
For questions, suggestions, or collaboration opportunities, reach out via [email protected] or open an issue in the repository.
Happy Experimenting & Building the Future of AI! 🤖✨
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