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1–14 of 14 · Most stars

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.

Sol3 (sol2 v3.0) - a C++ <-> Lua API wrapper with advanced features and top notch performance - is here, and it's great! Documentation:

  • sol
  • lua
  • lua-bindings
  • lua-binding

[INACTIVE] Avian is a lightweight virtual machine and class library designed to provide a useful subset of Java's features, suitable for building self-contained applications.

  • java
  • jit-compiler
  • openjdk
  • jvm

C++20 concurrency framework with no compromises. Excellent performance, powerful features, and simple syntax.

Rolisteam is a virtual tabletop. It helps you to manage tabletop role playing games with remote friends/players. It provides many features to share maps, pictures, dice roller, manage background music and much more. The main git repository is available here: [https://invent.kde.org/kde/rolisteam].

  • rpg
  • jdr
  • rpg-tool
  • c-plus-plus

Cancer Imaging Phenomics Toolkit (CaPTk) is a software platform to perform image analysis and predictive modeling tasks. Documentation: https://cbica.github.io/CaPTk

A 2D game engine developed in modern C++ using SDL, an Entity-Component-System (ECS) architecture, miniaudio, and Box2D. It features Lua scripting and is designed for web portability via WebAssembly

Implement a Linux gateway that reads serial port ( USB, serial , bluetooth) commands and transfers to MQTT host. MQTT without ethernet or Wifi on a low cost micocontroller. Don't develop a serial command interface , just use MQTT UI's and features.

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.