Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Solid | 518 | 5 years ago | 7 | mit | Python | |||||
🎯 A comprehensive gradient-free optimization framework written in Python | ||||||||||
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 | ||||||||||
Mth594_machinelearning | 306 | 7 years ago | 1 | Jupyter Notebook | ||||||
The materials for the course MTH 594 Advanced data mining: theory and applications (Dmitry Efimov, American University of Sharjah) | ||||||||||
Terrain Topology Algorithms | 298 | 2 years ago | 1 | mit | C# | |||||
Terrain topology algorithms in Unity | ||||||||||
Fmin | 295 | 366 | 23 | 6 years ago | 2 | November 25, 2016 | 5 | bsd-3-clause | JavaScript | |
Unconstrained function minimization in Javascript | ||||||||||
Stein Variational Gradient Descent | 261 | 5 years ago | 1 | mit | Python | |||||
code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm" | ||||||||||
Jstarcraft Ai | 201 | a year ago | 4 | apache-2.0 | Java | |||||
目标是提供一个完整的Java机器学习(Machine Learning/ML)框架,作为人工智能在学术界与工业界的桥梁. 让相关领域的研发人员能够在各种软硬件环境/数据结构/算法/模型之间无缝切换. 涵盖了从数据处理到模型的训练与评估各个环节,支持硬件加速和并行计算,是最快最全的Java机器学习库. | ||||||||||
Spinning Up Basic | 164 | 3 years ago | mit | Python | ||||||
Basic versions of agents from Spinning Up in Deep RL written in PyTorch | ||||||||||
Boml | 124 | 3 years ago | 8 | September 19, 2020 | 1 | mit | Python | |||
Bilevel Optimization Library in Python for Multi-Task and Meta Learning | ||||||||||
Clustering4ever | 109 | 3 | 3 years ago | 18 | May 03, 2020 | apache-2.0 | Scala | |||
C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering. |