# datawhalechina/daily-interview

Datawhale成员整理的面经，内容包括机器学习，CV，NLP，推荐，开发等，欢迎大家star

Repository: https://github.com/datawhalechina/daily-interview
Canonical: https://ross.abutalabs.com/products/daily-interview
Homepage: https://datawhalechina.github.io/daily-interview/
License: GPL-3.0
License Family: copyleft
Topics: interview-questions, cv, nlp, llm
Last push: 2026-07-08T05:55:09+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 91, release rhythm 35, longevity 100
- inputs: {"age_days": 2688, "days_push": 56, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3802, forks 494 (observed 2026-08-28T04:08:20.183952+00:00)

## What it is
A curated collection of technical interview questions and answers maintained by the Datawhale community, covering machine learning, computer vision, NLP, recommendation systems, and software development. It is organized into modules (algorithms, programming languages, CS fundamentals, AI, system design) with role-based study paths for quick pre-interview review.

## Use cases
- prepare for a machine learning engineer interview
- review common NLP and CV interview questions
- brush up on data structures and algorithms before an interview
- study system design questions for backend roles
- find a quick pre-interview refresher on CS fundamentals
- prepare answers for behavioral and project experience questions

## When to choose
- you want a concise, community-curated set of high-frequency interview topics rather than exhaustive material
- you are interviewing for AI/algorithm or software development roles in the Chinese tech ecosystem
- you need a fast half-day review right before an interview

## When to avoid
- you need a comprehensive textbook-style deep dive into any single topic
- you require content in languages other than Chinese
- you need interactive practice, mock interviews, or auto-graded exercises

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: tutorials, education, machine-learning, computer-vision, developer-tools
- platform: cross-platform
- tags: interview-preparation, interview-questions, cheatsheet, datawhale, job-hunting, algorithms, system-design, chinese, natural-language-processing, web-server

## Member repositories
- datawhalechina/daily-interview (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:20.183952+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 2026-08-29T18:26:48.322873+00:00, confidence not recorded.
  - readme: https://github.com/datawhalechina/daily-interview (fetched 2026-08-28T04:08:20.183952+00:00, sha bc4d419014dd)
  - homepage: https://datawhalechina.github.io/daily-interview/ (fetched 2026-08-29T09:22:01.208112+00:00, sha 36ce5573153e)
- Data as of 2026-08-30T08:39:29.467469+00:00.