# lcylmhlcy/Awesome-algorithm-interview

算法工程师(人工智能CV方向)面试问题及相关资料

Repository: https://github.com/lcylmhlcy/Awesome-algorithm-interview
Canonical: https://ross.abutalabs.com/products/awesome-algorithm-interview
License Family: other
Last push: 2024-08-18T18:21:48+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2778, "days_push": 745, "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 3031, forks 451 (observed 2026-08-28T04:07:38.920978+00:00)

## What it is
A curated awesome-list of interview questions and study resources for algorithm engineer positions focused on computer vision. It collects links covering machine learning, deep learning, C/C++, Python, competitions, and resume templates.

## Use cases
- prepare for an AI algorithm engineer interview
- find computer vision interview questions
- study machine learning and deep learning interview topics
- find PyTorch and TensorFlow learning resources
- get resume templates for ML engineering jobs

## When to choose
- you are interviewing for a CV/algorithm engineer role and want a curated link collection
- you want free community-compiled study material in one place

## When to avoid
- you need structured courses or verified answers rather than link lists
- you need non-CV algorithm interview prep (e.g., backend or data engineering)
- you need actively maintained content - updates appear infrequent

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: machine-learning, deep-learning, computer-vision, tutorials, awesome-lists
- platform: cross-platform
- tags: interview-preparation, algorithm-engineer, curated-list, cv, job-interview, chinese-language

## Member repositories
- lcylmhlcy/Awesome-algorithm-interview (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:38.920978+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 2026-08-29T18:46:51.773965+00:00, confidence not recorded.
  - readme: https://github.com/lcylmhlcy/Awesome-algorithm-interview (fetched 2026-08-28T04:07:38.920978+00:00, sha 203b12743505)
- Data as of 2026-08-30T08:39:29.467469+00:00.