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Facts
| Fact | facebookexperimental/Robyn Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community. | amanchadha/coursera-deep-learning-specialization Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models |
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| Stars | 1,536 | 4,421 |
| Net stars, last 30 days | +17 | +27 |
| Largest star day | +3 on Oct 22, 2026 | +5 on Jul 6, 2026 |
| Milestone reached in the record | None | None |
| Forks | 439 | 2,677 |
| Language | Jupyter Notebook | Jupyter Notebook |
| License | MIT | No license |
| Created | Apr 30, 2020 | Jun 24, 2020 |
| Last push | Nov 10, 2026 | Jun 10, 2026 |
| Archived | No | No |
| Health facts: Maintenance | 2 of 4 checks (2 without data) | 1 of 4 checks (2 without data) |
| Health facts: Releases | 1 of 3 checks (1 without data) | 0 of 3 checks (2 without data) |
| Health facts: Security practice | 0 of 1 check (1 without data) | 0 of 1 check (1 without data) |
| Health facts: Community files | 1 of 3 checks (2 without data) | 0 of 3 checks (2 without data) |
| Commits, last 13 weeks | No data | No data |
| Weeks with commits, last 52 | No data | No data |
| In the Repository Index since | Nov 13, 2026 | Nov 10, 2026 |
Topics that all of them have on the Repository Host: hyperparameter-optimization. The facts come from the Repository Host and from the Star History that AwesomeRepos records. A shared topic does not mean that one repository can replace another. Each Health Facts row is one dimension alone. AwesomeRepos does not add them into one score. How each check is defined