🧑🏫 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, ... 🧠
C-Rank Grade: D (Risky) - 10/100
External PR Merge Rate: -
Response Time: -
First Timer Success: -
labmlai/annotated_deep_learning_paper_implementations has a recorded first-timer success rate of 0.0%. Repositories ranked D typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in labmlai/annotated_deep_learning_paper_implementations respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 10.2 places labmlai/annotated_deep_learning_paper_implementations in the D tier.
The external contributor pull request merge rate for labmlai/annotated_deep_learning_paper_implementations is 0.0%, based on public PR activity from non-core contributors.
labmlai/annotated_deep_learning_paper_implementations does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
🧑🏫 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, ... 🧠
Established Python ecosystem in labmlai/annotated_deep_learning_paper_implementations. Maintains structured code review standards for external contributors.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/labmlai/annotated_deep_learning_paper_implementations?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Maintainers are most responsive around 11:00 UTC (11:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 09:00 to 13:00 UTC (09:00 - 13:00 UTC local).
Limited recent maintainer review activity recorded for this repository. Prediction based on heuristic baseline.
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (81% approval). Large PRs (>500 lines) take ~115 hours (29% approval). Keep your diff compact for rapid review.
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No cached good first issues currently tracked for labmlai/annotated_deep_learning_paper_implementations.
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When evaluating whether to contribute to labmlai/annotated_deep_learning_paper_implementations, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of labmlai/annotated_deep_learning_paper_implementations acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 0.0% were successfully merged into the primary branch.
labmlai/annotated_deep_learning_paper_implementations has a recorded first-timer success rate of 0.0%. Repositories ranked Risky typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 10.2 places labmlai/annotated_deep_learning_paper_implementations in the Risky tier.
The external pull request merge rate is 0.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
labmlai/annotated_deep_learning_paper_implementations does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for labmlai/annotated_deep_learning_paper_implementations are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.