# tdeboissiere/DeepLearningImplementations

Implementation of recent Deep Learning papers

Repository: https://github.com/tdeboissiere/DeepLearningImplementations
Canonical: https://ross.abutalabs.com/products/deeplearningimplementations
Language: Python
License: MIT
License Family: permissive
Topics: deep-learning-papers
Last push: 2020-10-23T10:03:46+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": 3644, "days_push": 2140, "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 1812, forks 638 (observed 2026-08-28T04:05:39.685036+00:00)

## What it is
A collection of Python implementations of recent deep learning research papers, including DenseNet, various GAN variants, pix2pix, InfoGAN, and SELU networks. It serves as educational reference code for understanding how published models are built.

## Use cases
- learn how GANs are implemented from papers
- study reference implementations of DenseNet
- understand pix2pix and InfoGAN code
- find example code for Wasserstein GAN
- learn self-normalizing networks from scratch

## When to choose
- you want readable reference code for classic deep learning papers
- you are learning how GAN architectures work in practice

## When to avoid
- you need production-ready or maintained deep learning code
- you need implementations of recent papers (last release 2020)
- you need a framework or library with a stable API

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: deep-learning, machine-learning, image-processing
- domain: deep-learning, machine-learning, computer-vision, tutorials
- platform: python
- tags: paper-implementations, gan, educational, research-code

## Member repositories
- tdeboissiere/DeepLearningImplementations (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:39.685036+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 2026-08-30T03:21:13.831809+00:00, confidence not recorded.
  - readme: https://github.com/tdeboissiere/DeepLearningImplementations (fetched 2026-08-28T04:05:39.685036+00:00, sha 6247cecbe5b7)
- Data as of 2026-08-30T08:39:29.467469+00:00.