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

暂无版本2,387star287fork同步于 2026-11-07 07:30 UTC

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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

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

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    README 原文

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    🎯 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

    1. Choose Your Path: Select a learning track that matches your goals
    2. Follow the Structure: Complete modules in the recommended order
    3. Practice: Implement the concepts through provided exercises
    4. Build: Create your own projects using the knowledge gained
    5. 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.

    1. Fork the repository
    2. Create your feature branch (git checkout -b feature/AmazingFeature)
    3. Commit your changes (git commit -m 'Add some AmazingFeature')
    4. Push to the branch (git push origin feature/AmazingFeature)
    5. Open a Pull Request

    📝 License

    This project is licensed under the terms of the MIT license. See the LICENSE file for details.