Repository Index

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.

• machine-learning
• statistics
• data-science
• data-analysis

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 🗄️

• acid
• database
• key-value-store
• document-database

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.

• analytics
• columnar-database
• cpp
• database

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