# labmlai/annotated_deep_learning_paper_implementations

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

Repository: https://github.com/labmlai/annotated_deep_learning_paper_implementations
Canonical: https://ross.abutalabs.com/products/annotated_deep_learning_paper_implementations
Homepage: https://nn.labml.ai
Language: Python
License: MIT
License Family: permissive
Topics: deep-learning, deep-learning-tutorial, pytorch, gan, transformers, reinforcement-learning, optimizers, neural-networks, transformer, machine-learning, attention, literate-programming, lora
Last push: 2026-01-22T04:26:00+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 63, release rhythm 35, longevity 100
- inputs: {"age_days": 2200, "days_push": 223, "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 67359, forks 6750 (observed 2026-08-28T04:12:20.832709+00:00)

## What it is
A collection of 60+ annotated PyTorch implementations of deep learning research papers, including transformers, optimizers, GANs, diffusion models, and reinforcement learning algorithms. Each implementation is documented with side-by-side explanations rendered as formatted notes to help readers understand the algorithms.

## Use cases
- learn how transformers work by reading annotated code
- understand the original transformer paper implementation
- study stable diffusion and DDPM implementations
- learn LoRA fine-tuning from scratch
- understand GAN variants like StyleGAN2 and CycleGAN
- study reinforcement learning algorithms like PPO and DQN
- learn how attention mechanisms like RoPE and ALiBi work

## When to choose
- you want to deeply understand deep learning papers through readable code
- you are learning PyTorch and neural network architectures
- you need reference implementations of classic and modern DL algorithms
- you prefer explanations alongside code rather than raw repos

## When to avoid
- you need production-ready, optimized model implementations
- you want a maintained library with stable APIs to build on
- you need frameworks with pre-trained model weights and pipelines

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, machine-learning, llm-training, reinforcement-learning, stable-diffusion
- domain: deep-learning, machine-learning, artificial-intelligence, tutorials, education
- platform: python
- tags: pytorch, annotated-papers, transformers, gans, literate-programming, tutorials

## Member repositories
- labmlai/annotated_deep_learning_paper_implementations (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:20.832709+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 2026-08-29T16:16:24.442499+00:00, confidence not recorded.
  - readme: https://github.com/labmlai/annotated_deep_learning_paper_implementations (fetched 2026-08-28T04:12:20.832709+00:00, sha c3993f9907ee)
  - homepage: https://nn.labml.ai (fetched 2026-08-28T17:52:26.151090+00:00, sha 6a32c46fadef)
- Data as of 2026-08-30T08:39:29.467469+00:00.