What it does This repo collects interview questions across two oddly paired domains: machine learning theory (decision trees, cross-validation, overfitting) and Go language internals (syntax, concurrency, implementation details). Questions are multiple-choice and short-answer format, with collapsible answers and brief explanations. Content is in Chinese, sourced from CMU exams and Andrew Ng’s Coursera course among others.

The interesting bit The pairing itself is the hook. Most interview repos pick a lane—either LeetCode grind or ML theory. This one assumes you might face both in the same hiring loop, which is increasingly common at Chinese tech companies. The Go sections drill implementation specifics (goroutine scheduling, memory model) rather than just “write a web server.”

Key highlights

• ~20 ML questions covering classic supervised learning concepts, with answers hidden behind <details> tags
• Four Go topic clusters: basic syntax, implementation internals, concurrency, and code-output puzzles
• Sources cited: CMU Machine Learning exams and Andrew Ng’s Coursera course
• Companion blog posts on geektutu.com with expanded explanations
• Jupyter Notebook repo structure, though content is primarily Markdown Q&A

Caveats

• Scope is narrow and static: only ~20 ML questions visible, Go depth unclear from README alone
• No interactive elements, no code to run—pure reading material
• “持续整理、更新” (continuously updated) is claimed, but last visible batch is ML 01-20 with no dates

Verdict Worth bookmarking if you’re interviewing at Chinese tech firms that mix ML system design with language-specific trivia. Skip it if you need hands-on coding practice or English-language resources.