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mixedreality-webrtc icon mixedreality-webrtc

MixedReality-WebRTC is a collection of components to help mixed reality app developers integrate audio and video real-time communication into their application and improve their collaborative experience

mixedreality213 icon mixedreality213

This Mixed Reality Academy course will explore ways of visualizing the motion controllers, handing input events and attaching custom UI elements to the controllers.

mixedrealitycompanionkit icon mixedrealitycompanionkit

This is a MixedRealityToolkit style repository for code bits and components that may not run directly on Microsoft HoloLens or immersive headsets but instead pair with them to build experiences.

mixedrealitydesignlabs_unity icon mixedrealitydesignlabs_unity

Mixed Reality Design Labs share experimental samples, explorations and learning from Windows Mixed Reality Design group. If you are looking for official toolkit, please use Mixed Reality Toolkit

mixedrealitytoolkit icon mixedrealitytoolkit

The MixedRealityToolkit is a collection of scripts and components intended to accelerate the development of mixed reality applications targeting Windows Mixed Reality.

mixedrealitytoolkit-unity icon mixedrealitytoolkit-unity

Mixed Reality Toolkit (MRTK) provides a set of components and features to accelerate cross-platform MR app development in Unity.

mixedrealitytoolkit-unreal icon mixedrealitytoolkit-unreal

The Mixed Reality Toolkit for Unreal provides a set of components and features to accelerate Mixed Reality app development with Unreal.

mixingboard icon mixingboard

a Knowledgeable Stylized Integrated Text Generation Platform

ml-server icon ml-server

GitHub repositories for Machine Learning Server

mlads2018-quantumml icon mlads2018-quantumml

Introduction to Quantum Machine Learning tutorial: Building a quantum perceptron using Microsoft Quantum Development Kit

mlflow.net icon mlflow.net

MLFlow.NET is a .NET Standard 2.0 based wrapper for the REST based MLFlow server API (https://mlflow.org/). The SDK package allows you to call the MLFlow server API from .NET apps. A sample use case is writing a .NET Core app that validates a machine learning model or API and stores the result in MLFlow

mlontheedgecodeproject icon mlontheedgecodeproject

The overall purpose of this document is to showcase an example of Azure Machine Learning on IoT Edge Devices using Microsoft Embedded Learning Library (ELL)

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