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.
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.
Uploading images to ASP.Net WebAPI, pre-processing, and storing in Azure Blob Storage without ever using the filesystem.
Findings from a pilot project where we identified reusable patterns, and ways to avoid common pitfalls when implementing object detection and tracking in Windows apps.
Computer vision is used in various industries to process camera data as part of an analytics pipeline. This post introduces a reusable pattern and set of algorithms for extracting objects of interest from depth camera data.
With the arrival of commodity depth-capable cameras, specifically, the Microsoft Kinect, as well as high-performance machine learning algorithms, entirely new capabilities are made possible. Working with academic experts and others, we are attempting to track the movements, body language, and vocalizations of dogs.
In this post, you'll see how we helped a startup to extract structured data from images - using Project Oxford's image recognition.
