Large-scale pretraining for dialogue

Visually explore, understand, and present your data.

This is a fun, new monospaced font that includes programming ligatures and is designed to enhance the modern look and feel of the Windows Terminal.

Sample code for Channel 9 Python for Beginners course

Free tech resources for faculty, students, and dev advocates for use in computer science courses, workshops, and hackathons.

Natural Language Processing Best Practices & Examples

Microsoft Web Template Studio quickly builds web applications using a wizard-based UI to turn your needs into a foundation of best patterns and practices

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

The new Windows Terminal and the original Windows console host, all in the same place!

Tutorials and programming exercises for learning Q# and quantum computing

The source for the Linux kernel used in Windows Subsystem for Linux 2 (WSL2)

MASS: Masked Sequence to Sequence Pre-training for Language Generation

Insider Dev Tour - Demos - 2019

Debugging, monitoring and visualization for Python Machine Learning and Data Science

A distributed approximate nearest neighborhood search (ANN) library which provides a high quality vector index build, search and distributed online serving toolkits for large scale vector search scenario.

Microsoft Build Accelerator

Cryptographic library

NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

A lightning fast Finite State machine and REgular expression manipulation library.

The Bosque programming language is an experiment in regularized design for a machine assisted rapid and reliable software development lifecycle.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Frontend Workshop from HTML/CSS/JS to TypeScript/React/Redux