awesome-gradient-boosting-papers
stidler
| awesome-gradient-boosting-papers | stidler | |
|---|---|---|
| 1 | 3 | |
| 981 | 45 | |
| - | - | |
| 3.7 | 10.0 | |
| about 2 months ago | about 1 year ago | |
| Python | ||
| Creative Commons Zero v1.0 Universal | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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awesome-gradient-boosting-papers
stidler
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"Anytime someone puts a lock on something you own, against your wishes, and doesn't give you the key, they're not doing it for your benefit". However, people seem to like it. The sorry state of Android Backups
Yup, the local backup will contain most app data/config by default, but developers can hide stuff from it, like how the Steam app has hidden the TOTP codes from it https://github.com/SteamTimeIdler/stidler/issues/14
- I found someone who has "Steam" in recent activity, how?
- Vaultwarden 1.25.0 released with greatly improved synchronization speed
What are some alternatives?
Unredactor - In this project we are tryinbg to create unredactor. Unredactor will take a redacted document and the redacted flag as input, inreturn it will give the most likely candidates to fill in redacted location. In this project we are only considered about unredacting names only. The data that we are considering is imdb data set with many review files. These files are used to buils corpora for finding tfidf score. Few files are used to train and in these files names are redacted and written into redacted folder. These redacted files are used for testing and different classification models are built to predict the probabilies of each class. Top 5 classes i.e names similar to the test features are written at the end of text in unreddacted foleder.
SharpLearning - Machine learning for C# .Net
awesome-fraud-detection-papers - A curated list of data mining papers about fraud detection.
Intrusion-Detection-System-Using-Machine-Learning - Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation