datawhalechina/key-book
A companion reading guide for the book 'Introduction to Machine Learning Theory' written in Jupyter Notebook format.
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This repository serves as a study companion for the Chinese textbook ‘Introduction to Machine Learning Theory’ authored by Zhou Zhihua and colleagues. It supplements the original text with detailed proofs, concept explanations, and case studies to help readers navigate the theoretical aspects of machine learning. The material covers seven core theoretical concepts: learnability, complexity, generalization bounds, stability, consistency, convergence rates, and regret bounds. The content is maintained as Jupyter Notebooks, making it interactive and accessible for learners.
Frequently asked
- What is datawhalechina/key-book?
- A companion reading guide for the book 'Introduction to Machine Learning Theory' written in Jupyter Notebook format.
- Is key-book open source?
- Yes — datawhalechina/key-book is an open-source project tracked on heatdrop.
- What language is key-book written in?
- datawhalechina/key-book is primarily written in Jupyter Notebook.
- How popular is key-book?
- datawhalechina/key-book has 1.7k stars on GitHub.
- Where can I find key-book?
- datawhalechina/key-book is on GitHub at https://github.com/datawhalechina/key-book.