awesome-data-valuation
💱 A curated list of data valuation (DV) to design your next data marketplace (by daviddao)
Mathematics-for-ML
🧮 A collection of resources to learn mathematics for machine learning (by dair-ai)
| awesome-data-valuation | Mathematics-for-ML | |
|---|---|---|
| 1 | 2 | |
| 86 | 4,172 | |
| - | 2.7% | |
| 6.2 | 0.0 | |
| 5 months ago | over 1 year ago | |
| MIT License | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-data-valuation
Posts with mentions or reviews of awesome-data-valuation.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[D] Determining quality of training images with some metrics
You can find more papers at the GitHub repo awesome-data-valuation.
Mathematics-for-ML
Posts with mentions or reviews of Mathematics-for-ML.
We have used some of these posts to build our list of alternatives
and similar projects.
What are some alternatives?
When comparing awesome-data-valuation and Mathematics-for-ML you can also consider the following projects:
Data-Science-Roadmap - Data Science Roadmap from A to Z
pycm - Multi-class confusion matrix library in Python
ml-blogs-that-are-worth-reading - Blogs on Machine Learning and Deep learning