# 315386775/DeepLearing-Interview-Awesome-2024

AIGC-interview/CV-interview/LLMs-interview面试问题与答案集合仓，同时包含工作和科研过程中的新想法、新问题、新资源与新项目

Repository: https://github.com/315386775/DeepLearing-Interview-Awesome-2024
Canonical: https://ross.abutalabs.com/products/deeplearing-interview-awesome-2024
License Family: other
Topics: aigc, algorithms, awesome-cv, cnn, deep-learning, interview-questions, leetcode-python, machine-learning, medical-imaging, pytorch, self-driving-car
Last push: 2026-03-05T12:56:25+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 70, release rhythm 35, longevity 100
- inputs: {"age_days": 3085, "days_push": 181, "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 2885, forks 244 (observed 2026-08-28T04:07:28.176704+00:00)

## What it is
A curated collection of deep learning, LLM, and computer vision interview questions with detailed answers, organized into six topic modules including AIGC, perception algorithms, and hand-written code exercises. It also collects new ideas, resources, and projects from research and industry work, updated continuously.

## Use cases
- prepare for deep learning algorithm interviews
- study LLM interview questions with answers
- review computer vision and perception algorithm interview topics
- practice hand-written ML code for interviews
- find curated resources on large language models and AIGC
- learn LoRA and fine-tuning concepts for job interviews

## When to choose
- you are preparing for ML/DL algorithm engineer interviews at tech companies
- you want a continuously updated question bank covering LLMs, CV, and AIGC
- you prefer Chinese-language explanations with detailed answer references

## When to avoid
- you need a structured course or textbook rather than a question-answer list
- you need English-language interview material
- you want runnable production code rather than study notes

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: deep-learning, machine-learning, large-language-models, computer-vision, tutorials, awesome-lists
- platform: python, cross-platform
- tags: interview-questions, llms, aigc, pytorch, leetcode, self-driving, medical-imaging, chinese-language

## Member repositories
- 315386775/DeepLearing-Interview-Awesome-2024 (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:28.176704+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 2026-08-30T07:35:23.841897+00:00, confidence not recorded.
  - readme: https://github.com/315386775/DeepLearing-Interview-Awesome-2024 (fetched 2026-08-28T04:07:28.176704+00:00, sha 3cb4b2af4944)
- Data as of 2026-08-30T08:39:29.467469+00:00.