这是教程与文档仓库,不是可直接安装运行的软件包;部署、微调、AI Agents、Projects 等路线标注为 Coming Soon,内容尚未齐全;README 未给出依赖安装或运行环境说明,需按各 docs 子目录自行准备
AI-Engineering.academy
adithya-s-kMIT更新放缓一套面向应用 AI 的实战教程,用 Jupyter Notebook 讲透大模型微调、推理与量化,帮工程师把 LLM 真正跑起来。
Mastering Applied AI, One Concept at a Time
git clone https://github.com/adithya-s-k/AI-Engineering.academy.gitREADME 未说明
面向应用型 GenAI 的结构化学习路线仓库,帮你从零散教程里理清该学什么、按什么顺序学
一套面向应用 AI 的实战教程,用 Jupyter Notebook 讲透大模型微调、推理与量化,帮工程师把 LLM 真正跑起来。
核心能力
- 按 Prompt Engineering、RAG、微调、部署、AI Agents 划分学习路径
- RAG 路线强调不依赖框架从零实现系统
- 覆盖 RAG 系统选型与生产部署优化
- 提供端到端实战项目与练习
- MIT 许可,可自由 fork 与贡献内容
容易踩的坑
暂无 release 数据(可能没有发版,或尚未同步)。
用户点评
登录用户的真实使用记录,可按版本和硬件筛选还没有人点评。第一个说出真实体验的人最有价值。
🎯 Mission
Your journey into AI shouldn't be overwhelming. AIengineering.academy curate and organize essential knowledge into clear learning paths, making complex AI concepts accessible and practical for everyone.
🌟 Why Choose AI Engineering Academy?
- 📚 Structured Learning: Carefully designed pathways from fundamentals to advanced concepts
- 💻 Hands-on Practice: Real-world projects and implementations
- 🎓 Industry-Aligned: Focus on practical, production-ready skills
- 🤝 Community-Driven: Learn alongside peers and experts
🗺️ Learning Paths
1. Prompt Engineering
Master the art of effectively communicating with AI models
- Fundamental concepts and best practices
- Advanced techniques for optimal results
- Real-world applications and case studies
2. Retrieval Augmented Generation (RAG)
Enhance AI responses with external knowledge
- Core RAG architecture and components
- Building RAG systems from scratch
- Production deployment strategies
- Performance optimization techniques
3. Fine-tuning
Customize AI models for your specific needs
- Understanding fine-tuning fundamentals
- Model adaptation techniques
- Best practices and common pitfalls
- Resource optimization
4. Deployment 📍 Coming Soon
Take your AI models from laptop to production
- Cloud deployment strategies
- Performance optimization
- Scaling considerations
- Monitoring and maintenance
5. AI Agents
Build autonomous AI systems
- Agent architectures
- Decision-making frameworks
- Multi-agent systems
- Real-world applications
6. Projects
Apply your knowledge through hands-on projects
- End-to-end implementations
- Industry-relevant scenarios
- Portfolio-worthy demonstrations
🚀 Getting Started
- Choose Your Path: Select a learning track that matches your goals
- Follow the Structure: Complete modules in the recommended order
- Practice: Implement the concepts through provided exercises
- Build: Create your own projects using the knowledge gained
- Share: Contribute to the community and help others learn
👥 Community
- Join our growing community of AI enthusiasts
- Share your learning journey
- Collaborate on projects
- Get help when you're stuck
- Contribute to improving the curriculum
🤝 Contributing
We welcome contributions! Whether it's fixing a typo, adding new content, or suggesting improvements, every contribution helps make AI Engineering Academy better for everyone.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
📝 License
This project is licensed under the terms of the MIT license. See the LICENSE file for details.