In early 2018, Microsoft partnered with a successful international online fashion retailer to develop an efficient system for logging newly arrived apparel items and quickly determining whether the item is already in stock

We explored possible techniques to identify duplicate catalogue entries in fashion retail through image similarity with deep learning and computer vision.

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...

Using Microsoft Cognitive Services Vision API Optical Character Recognition within Azure ML Studio to Predict Expense Type from Receipts.

An examination of whether a more sophisticated learner will always result in better performance in a text-based classifier, and the trade-off between accuracy and training time.

Investigations that provide insights on how to choose an N-gram feature that maximizes performance of a classifier.

In this code story, we explore how the topology of a deep neural network can affect the performance of a text-based classifier.

Utilising deep learning to detect emotions from short, informal English text.

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.