ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Updated
Oct 21, 2025 - C++
scikit-learn is a widely-used Python module for classic machine learning. It is built on top of SciPy.
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Fit interpretable models. Explain blackbox machine learning.
Samples and Tools for Windows ML.
Fast Best-Subset Selection Library
Framework of vectorized and distributed data analytics
APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.
Machine Learning Models Deployment using C++ Code Generation
A tool built on top of OpenFace to detect eye contact with babies.
An active vision system on the PR2 humanoid robot to dynamically detect objects via the head and arm cameras
Python-Wrapper for Francesco Parrella's OnlineSVR C++ implementation with scikit-learn-compatible interface.
A tiny C++ Time Series Database library designed for compatibility with the PyData Ecosystem.
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
A scikit-learn implementation of BOOMER - An Algorithm for Learning Gradient Boosted Multi-Output Rules
This is my implementation of the 3D Pick and Place project for the Udacity Robotics Nanodegree. We perform multiple processes to segment a point cloud into its object components and use scikit-learn to do object recognition. A PR2 robot then performs a pick and place operation on the recognized objects in simulation with ROS and Gazebo.
Auto-ML based on a coevolutionary model.
pENC is a fast, hybrid C++/Python toolkit for parallel audio feature extraction and classification. It uses OpenCL (GPU) and Cython (CPU) for accelerated MFCC extraction, supports batch processing, and integrates with scikit-learn for machine learning. Ideal for research and large-scale audio analysis.
Scikit-learn-compatible python3 wrapper for Tsetlini - a Tsetlin Machine learning model
Deploys an optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on Arduino UNO board
Created by David Cournapeau
Released January 05, 2010
Latest release about 1 month ago