# zhengjingwei/machine-learning-interview

算法工程师-机器学习面试题总结

Repository: https://github.com/zhengjingwei/machine-learning-interview
Canonical: https://ross.abutalabs.com/products/zhengjingwei-machine-learning-interview
License Family: other
Topics: machine-learning, deep-learning, interview
Last push: 2019-09-26T13:16:03+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2704, "days_push": 2533, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1695, forks 217 (observed 2026-08-28T04:05:23.631073+00:00)

## What it is
A curated collection of machine learning and deep learning interview questions with answers, written primarily in Chinese. It covers fundamentals like loss functions, evaluation metrics, feature engineering, and classic algorithms such as KNN, SVM, and Naive Bayes.

## Use cases
- prepare for a machine learning engineer interview
- review classic ML algorithm concepts before an interview
- study feature engineering interview questions
- practice deep learning interview questions
- refresh knowledge of SVM, KNN, and Naive Bayes theory

## When to choose
- you are preparing for algorithm/ML engineer interviews and want a structured question bank
- you prefer Chinese-language study material
- you want a checklist-style review of ML fundamentals

## When to avoid
- you need a runnable library or code tool
- you need up-to-date content on LLMs or modern deep learning topics
- you need English-language material or a maintained, actively updated resource

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, nlp
- domain: machine-learning, tutorials, education
- platform: cross-platform
- tags: interview-preparation, question-bank, chinese-language, study-notes

## Member repositories
- zhengjingwei/machine-learning-interview (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:23.631073+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:37:58.559081+00:00, confidence not recorded.
  - readme: https://github.com/zhengjingwei/machine-learning-interview (fetched 2026-08-28T04:05:23.631073+00:00, sha db16ebb49b79)
- Data as of 2026-08-30T08:39:29.467469+00:00.