Claiming expenses is usually a manual process. This project aims to improve the efficiency of receipt processing by looking into ways to automate this process. This code story describes how we created a skeletal framework to achieve the following: We found a few challenges in addressing these goals. For instance, the quality of...
We collaborated on an image classification pipeline to perform automatic face detection and matching using machine learning via Microsoft Cognitive Services Face API. Our project was built with Azure Functions to process images using message queues.
We use IoT sensors to collect positional and motion data from professional and amateur skiers to classify expertise and skill level through machine learning.
Microsoft has partnered with Getty Images to explore how Neural Nets could be used to transform the stock photo industry.
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.
Creating an object detection model using Microsoft's open source deep learning framework CNTK and its implementation of Fast-RCNN.
Developing a frame-by-frame video tagging tool for video processing and image recognition.
In this real-life-code story, we show how we used Cognitive Services and LUIS to build a vehicle center console that can listen and respond to user's commands, specifically focusing on determining intent.
In this code story, we consider how data preparation can impact the performance of a classifier, and how that may lead to a refinement of problem statement, i.e. the important question we are asking.
In this code story, we explore how the topology of a deep neural network can affect the performance of a text-based classifier.
