Ad Preditor
7th in a competition organised by ICT
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Xgboost | 25,253 |  | 796 | 972 | 3 months ago | 79 | November 13, 2023 | 412 | apache-2.0 | C++ |
| Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow | ||||||||||
| Lightgbm | 15,999 |  | 278 | 574 | 13 days ago | 34 | September 12, 2023 | 345 | mit | C++ |
| A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. | ||||||||||
| Ml Nlp | 10,874 | 2 years ago | 29 | Jupyter Notebook | ||||||
| 此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。 | ||||||||||
| Mlalgorithms | 9,318 | a year ago | 10 | mit | Python | |||||
| Minimal and clean examples of machine learning algorithms implementations | ||||||||||
| Catboost | 7,564 | 12 | 3 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. | ||||||||||
| Recsys | 815 | 4 years ago | ||||||||
| 计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估 | ||||||||||
| Thundergbm | 674 | 7 months ago | 25 | September 19, 2022 | 36 | apache-2.0 | C++ | |||
| ThunderGBM: Fast GBDTs and Random Forests on GPUs | ||||||||||
| Ml Note | 617 | a year ago | mit | |||||||
| :orange_book:慢慢整理所学的机器学习算法，并根据自己所理解的样子叙述出来。(注重数学推导) | ||||||||||
| Daily Deeplearning | 532 | 3 months ago | mit | Jupyter Notebook | ||||||
| 🔥机器学习/深度学习/Python/算法面试/自然语言处理教程/剑指offer/machine learning/deeplearning/Python/Algorithm interview/NLP Tutorial | ||||||||||
| Chefboost | 428 | 4 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 | ||||||||||
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