ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Fit interpretable models. Explain blackbox machine learning.

Samples and Tools for Windows ML.

Samples for Intel® oneAPI Toolkits

Fast Best-Subset Selection Library

Framework of vectorized and distributed data analytics

A tool built on top of OpenFace to detect eye contact with babies.

Machine Learning Models Deployment using C++ Code Generation

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.

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.

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

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

Deploys an optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on ESP32-S3 dev kit

Deploys a vanilla Decision Tree for Arrhythmia classification using Chapman ECG dataset on Arduino UNO board

Deploys a vanilla non-optimized Decision Tree for Arrhythmia classification using Chapman ECG dataset on ESP32-S3 dev kit