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Microsoft's Projects

davinci icon davinci

DaVinci toolkit aims at high-quality multimedia content creation which plays an important role in modern work and life. The targeted features can include both low-level image and video enhancement tasks, and high-level visual editing tasks.

dbfs icon dbfs

A tool for mounting MS SQL Server DMVs and custom queries using FUSE.

dbgshell icon dbgshell

A PowerShell front-end for the Windows debugger engine.

dchemistruck icon dchemistruck

A collection of scripts to facilitate management of Microsoft Defender XDR products + Sentinel.

dcmkit icon dcmkit

Datacenter Migration Program Kit

dcp icon dcp

Developer Control Plane API server and CLI.

dcuctip icon dcuctip

Microsoft Digital Crimes Unit Cyber Threat Intelligence Program

dcvc icon dcvc

Deep Contextual Video Compression

dds icon dds

DDS: DPU-optimized Disaggregated Storage

deadlettermonitor icon deadlettermonitor

DeadLetterMonitor is a tool that automatically deals, according to custom rules, with some errors that can occur in Pub/Sub Architectures.

debiasing-item2item icon debiasing-item2item

Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)

debug-gym icon debug-gym

A Text-Based Environment for Interactive Debugging

debugmcp icon debugmcp

Gift your VS Code agent a real debugger: breakpoints, stepping, inspection.

debugpy icon debugpy

An implementation of the Debug Adapter Protocol for Python

decisiondiagrams icon decisiondiagrams

A performant and ergonomic implementation of binary decision diagrams for .NET

deck icon deck

New and improved management UI

decodingsuperusage icon decodingsuperusage

Toolkit to help you analyze what super users of Copilot do differently, learn repeatable patterns, and drive adoption across the company.

dedector icon dedector

DEDector: Smartphone-Based Noninvasive Screening of Dry Eye Disease

deep-language-networks icon deep-language-networks

We view Large Language Models as stochastic language layers in a network, where the learnable parameters are the natural language prompts at each layer. We stack two such layers, feeding the output of one layer to the next. We call the stacked architecture a Deep Language Network - DLN

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