Democratizing AI Expertise and Technology for All
Artificial Intelligence (AI) has transformed from a niche specialty into a cornerstone of modern technology, influencing countless aspects of our daily lives and work.
This repository houses the GenAI course, an initiative dedicated to democratizing AI expertise and technology. We aim to provide both practical skills and theoretical knowledge for innovations, making AI accessible to everyone from lab researchers to industry professionals seeking to enhance their skill sets.
| Purpose | Audience | Level |
|---|---|---|
| 🎯 Build a robust foundation in Generative AI | 👥 Learners at all levels | 📈 From beginners to researchers |
| 🧠 Foster critical thinking and creativity | 🎓 Students & professionals | 🔬 Academic & industry focus |
| Core Topics | Advanced Topics | Applications |
|---|---|---|
| 🤖 Large Language Models | 🎨 Diffusion Models | 🔍 RAG Systems |
| 🏗️ Transformer Architecture | 🎯 Human Value Alignment | 🚀 Efficient Deployment |
| 🎓 Fine-tuning Techniques | 🤝 Agentic AI | 🛡️ AI Safety |
# 1. Clone the repository
git clone https://github.com/JieGroup/GenAI.git
cd GenAI
# 2. Install dependencies
pip install -r docs/requirements.txt
# 3. Start learning!
# Visit: https://jiegroup.github.io/GenAI/| Module | Topic | Duration | Difficulty |
|---|---|---|---|
| 📖 Introduction | Course Overview & Setup | 1 week | ⭐ |
| 🧠 Quick Review | Deep Learning Fundamentals | 2 weeks | ⭐⭐ |
| 🤖 LLMs | Large Language Models | 3 weeks | ⭐⭐⭐ |
| 🏗️ Training | Training from Scratch | 3 weeks | ⭐⭐⭐⭐ |
| 🎯 Fine-tuning | Model Adaptation | 2 weeks | ⭐⭐⭐ |
| 🤝 Alignment | Human Value Alignment | 2 weeks | ⭐⭐⭐⭐ |
| 🎨 Diffusion | Diffusion Models | 2 weeks | ⭐⭐⭐⭐ |
| 🔍 RAG | Retrieval Augmented Generation | 2 weeks | ⭐⭐⭐ |
| 🚀 Deployment | Efficient Deployment | 2 weeks | ⭐⭐⭐⭐ |
| 🛡️ Safety | AI Safety & Security | 2 weeks | ⭐⭐⭐⭐ |
| Feature | Description | Benefit |
|---|---|---|
| 📚 Comprehensive Curriculum | 10+ modules covering all aspects of GenAI | Complete learning path |
| 💻 Hands-on Code | Self-contained, tested implementations | Practical experience |
| 🎓 Academic Quality | University-level course materials | Rigorous foundation |
| 🌍 Open Source | Free and accessible to everyone | Democratized education |
| 🔄 Regular Updates | Continuously maintained and improved | Latest developments |
We thrive on community contributions! Whether it's enhancing documentation, proposing new features, or fixing bugs, your input is valuable.
| Contribution Type | How to Help | Impact |
|---|---|---|
| 📝 Documentation | Improve explanations, add examples | Help others learn |
| 🐛 Bug Reports | Report issues, suggest fixes | Improve quality |
| 💡 Feature Requests | Propose new topics, exercises | Enhance curriculum |
| 🔧 Code Improvements | Optimize implementations | Better performance |
# Fork the repository
# Make your changes
# Submit a pull request📖 Read our Contributing Guidelines for detailed information.
This course is developed by Prof. Jie Ding at the University of Minnesota's School of Statistics.
This course was created from scratch by Prof. Jie Ding. Key contributors include An Luo and Fangqiao Tian as Teaching Assistants, and Ganghua Wang for technical support. Special thanks to the School of Statistics and Minnesota Supercomputing Institute (MSI) for their institutional support.
| License | Code of Conduct | Contributing |
|---|---|---|
 |
 |
 |
Love this course? Give us a star on GitHub! It helps others discover this resource and shows your support for open-source AI education.
Made with ❤️ by the University of Minnesota GenAI Team
This course was conceived and developed by Professor Jie Ding from the School of Statistics at the University of Minnesota. The School of Statistics and the Minnesota Supercomputing Institute (MSI) are acknowledged for their administrative support and provision of computation and educational resources. Special thanks go to Xun Xian, Ganghua Wang, Jin Du, An Luo, Xinran Wang, Qi Le, Harsh Shah, Jiawei Zhang, Enmao Diao, Michael Coughlin, and Ali Anwar, who provided significant contributions to the course design and development.