Synapsa Commons: free, hands-on AI courses that run anywhere (Colab, Kaggle, Binder, Codespaces, Jupyter). EU AI Act conformity evidence, model validation, predictive maintenance, document extraction, retrieval and RAG, AI agents and MCP, a simulated humanoid. Every lesson autograded. From the team building Synapsa.
C-Rank Grade: D (Risky) - 8/100
External PR Merge Rate: -
Response Time: -
First Timer Success: -
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials 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 TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials 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 7.5 places TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials in the D tier.
The external contributor pull request merge rate for TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials is 0.0%, based on public PR activity from non-core contributors.
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Synapsa Commons: free, hands-on AI courses that run anywhere (Colab, Kaggle, Binder, Codespaces, Jupyter). EU AI Act conformity evidence, model validation, predictive maintenance, document extraction, retrieval and RAG, AI agents and MCP, a simulated humanoid. Every lesson autograded. From the team building Synapsa.
Collaborative maintainer environment in TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials. Reviews community pull requests with focus on project quality.
Discussions in TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
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/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials?utm_source=github&utm_medium=badge)
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap
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.
No cached good first issues currently tracked for TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials.
Rate what actually matters after opening a pull request here.
Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.
When evaluating whether to contribute to TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials, 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 TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials 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.
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials 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 7.5 places TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials 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.
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials 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 TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials 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.