AI-Notes and AI-Practices
These are **complements** — the former provides theoretical foundations (mathematics, statistics, NLP theory) while the latter offers hands-on implementations (linear regression, CNN, RNN tutorials), making them suitable for sequential learning from theory to practice.
About AI-Notes
wx-chevalier/AI-Notes
:books: [.md & .ipynb] Series of Artificial Intelligence & Deep Learning, including Mathematics Fundamentals, Python Practices, NLP Application, etc. 💫 人工智能与深度学习实战，数理统计篇 | 机器学习篇 | 深度学习篇 | 自然语言处理篇 | 工具实践 Scikit & Tensoflow & PyTorch 篇 | 行业应用 & 课程笔记
This project offers a comprehensive collection of notes and practical examples for understanding and applying Artificial Intelligence, Machine Learning, and Deep Learning concepts. It takes in theoretical foundations and code examples, primarily in Jupyter Notebooks, to provide clear explanations and practical implementations. Data scientists, machine learning engineers, and students looking to master AI applications will find this resource invaluable.
About AI-Practices
zimingttkx/AI-Practices
🎓 机器学习与深度学习实战教程 | Comprehensive ML & DL Tutorial with Jupyter Notebooks | 包含线性回归、神经网络、CNN、RNN等完整教程
This project offers a complete, hands-on learning platform for artificial intelligence, machine learning, and deep learning. It guides you from foundational math to advanced techniques through interactive notebooks and practical examples. Whether you're a data scientist, AI researcher, or machine learning engineer, you can learn to build and deploy complex AI systems.
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