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.
- gbdt
- gbm
- machine-learning
- data-mining
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A toolkit for making real world machine learning and data analysis applications in C++
- machine-learning
- deep-learning
- c-plus-plus
- python
Open3D: A Modern Library for 3D Data Processing
- mesh-processing
- computer-graphics
- opengl
- cpp
cuDF - GPU DataFrame Library
- gpu
- rapids
- cudf
- arrow
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.
- machine-learning
- decision-trees
- gradient-boosting
- gbm
C++ implementation of the Python Numpy library
- python
- numpy
- c-plus-plus
- numerical-analysis
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
A Python package for manipulating 2-dimensional tabular data structures
- python
- data-analysis
- data-structure
- performance
SimpleITK: a layer built on top of the Insight Toolkit (ITK), intended to simplify and facilitate ITK's use in rapid prototyping, education and interpreted languages.
- simpleitk
- c-plus-plus
- swig
- registration
Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️
The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.
- pointclouds
- 3-d
- visualization
- lidar
TTK - Topological Data Analysis and Visualization - Source Code
- topological-data-analysis
- visualization
- python
- paraview
An experimental embedded SQL engine in C++20. Query Parquet, CSV, JSON, Arrow, Avro, SQLite, and Excel files directly with SQL, in-process. Early-stage.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
- svm
- regression
- classification
- machine-learning