# ndb796/Deep-Learning-Paper-Review-and-Practice

꼼꼼한 딥러닝 논문 리뷰와 코드 실습

Repository: https://github.com/ndb796/Deep-Learning-Paper-Review-and-Practice
Canonical: https://ross.abutalabs.com/products/deep-learning-paper-review-and-practice
Language: Jupyter Notebook
License Family: other
Topics: deep-learning, ai, paper-reviews
Last push: 2022-06-28T07:26:08+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": 2192, "days_push": 1527, "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 1171, forks 330 (observed 2026-08-28T04:03:51.397239+00:00)

## What it is
A Korean-language educational repository collecting thorough reviews of popular deep learning papers with accompanying code practice notebooks. It covers image recognition and NLP papers such as ResNet, DETR, Transformer, and BERT, with video reviews, summary PDFs, and PyTorch-style Jupyter implementations.

## Use cases
- learn deep learning by implementing papers
- understand the transformer architecture with code
- study ResNet with MNIST and CIFAR-10 notebooks
- review BERT paper with practical examples
- learn style transfer from classic papers
- find summaries of popular computer vision papers

## When to choose
- you prefer learning papers alongside runnable Jupyter notebook implementations
- you want Korean-language explanations of landmark deep learning papers
- you need curated links to papers, videos, and summary PDFs in one place

## When to avoid
- you need production-ready or maintained deep learning libraries
- you require English-only learning materials
- you need up-to-date coverage of the latest papers, as the repo has not been updated since 2022

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, nlp, computer-vision
- domain: deep-learning, machine-learning, computer-vision, tutorials
- platform: python
- tags: paper-reviews, jupyter-notebooks, korean, educational, transformers, style-transfer, natural-language-processing

## Member repositories
- ndb796/Deep-Learning-Paper-Review-and-Practice (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:51.397239+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 2026-08-30T06:28:32.616499+00:00, confidence not recorded.
  - readme: https://github.com/ndb796/Deep-Learning-Paper-Review-and-Practice (fetched 2026-08-28T04:03:51.397239+00:00, sha fb1001c5c67f)
- Data as of 2026-08-30T08:39:29.467469+00:00.