Data Science Notes
Various resources on advanced analytics and beyond
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Catboost | 7,564 | 12 | 5 months ago | 20 | September 19, 2023 | 539 | apache-2.0 | Python | ||
| A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. | ||||||||||
| Chefboost | 428 | 6 months ago | 17 | February 16, 2022 | mit | Python | ||||
| A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python | ||||||||||
| Awesome Distributed Deep Learning | 309 | 3 years ago | ||||||||
| A curated list of awesome Distributed Deep Learning resources. | ||||||||||
| Learningx | 213 | 4 years ago | 3 | mit | Python | |||||
| Deep & Classical Reinforcement Learning + Machine Learning Examples in Python | ||||||||||
| Autograd.jl | 160 | 2 years ago | 19 | other | Julia | |||||
| Julia port of the Python autograd package. | ||||||||||
| Mariana | 151 | 6 years ago | 4 | apache-2.0 | JavaScript | |||||
| The Cutest Deep Learning Framework which is also a wonderful Declarative Language | ||||||||||
| Notebooks | 78 | 3 years ago | mit | Jupyter Notebook | ||||||
| A collection of Jupyter/IPython notebooks | ||||||||||
| Jlboost.jl | 61 | 3 years ago | 18 | mit | Julia | |||||
| A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms | ||||||||||
| Housing | 34 | 2 years ago | 6 | May 08, 2021 | mit | PHP | ||||
| An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine. | ||||||||||
| Credit | 18 | 2 years ago | 3 | January 01, 2021 | mit | PHP | ||||
| An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset. | ||||||||||
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