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Jun 23, 2017
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Building Facebook.Yoga for UWP and WPF

Eric Rozell

We recently worked to bring greater parity to react-native-windows by building cross-platform layout library Facebook.Yoga for UWP and WPF as the React Native framework for iOS and Android switched to use Yoga.

Jun 12, 2017
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Learning Image to Image Translation with Generative Adversarial Networks

Olga Liakhovich

Microsoft has partnered with Getty Images to explore how Neural Nets could be used to transform the stock photo industry.

Jun 6, 2017
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Geocoding Social Conversations with NLP and JavaScript

Anastasia Zolochevska

Using Azure Functions and the open-source JavaScript library Compromise for inferring location in social media posts.

Jun 1, 2017
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Deploying bots using the Serverless framework

Ville Rantala

Microsoft partnered with MaaS Global to build a bot using the Microsoft Bot Framework Node.js SDK that can be deployed with the Serverless framework.

May 31, 2017
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Digital badge building with reusable Web Components

David Douglas

Designing and developing the badge builder for the Duke of York's iDEA programme with responsive web design, web components and Azure.

May 31, 2017
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A Production Deis Workflow Deployment on Azure

James Spring

We will cover the steps necessary to get Deis Workflow up and running on Azure, and address the production deployment steps in depth.

May 12, 2017
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Food Classification with Custom Vision Service

Olga Liakhovich

How we created an app to identify foods and their nutritional content by using the new Custom Vision Service to leverage domain-specific image recognition powered by DNNs.

May 10, 2017
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Project Fortis: Accelerating UN Humanitarian Aid Planning with GraphQL

Erik Schlegel

Using GraphQL and Azure to create a data processing pipeline for identifying trends and providing insights about global humanitarian crises.

May 10, 2017
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Classifying Leaks Using Cognitive Toolkit

Michael Lanzetta

We use Deep Learning to turn a painful and time-consuming leak-detection task for water and oil pipelines into a fast, painless process. Using Python and Fast Fourier Transforms, we turn audio sensor data into images, then use Convolutional Neural Networks to detect and classify pipeline anomalies.