This blog focuses on developing an algorithm to understand spoken Arabic digits. Such algorithms are the first step in developing computers that can understand languages with applications ranging from text-to-speech, voice-recognition, and translation, to modern AI assistants that are widely becoming available.
Document classification is currently one of the most important branches of Natural Language Processing (NLP). The general idea is to automatically classify documents into categories using machine learning algorithms.
The applications are almost endless, we can classify: patient records, movie reviews, webpages, emails (spam vs not spam), and in fact anything text based.
The goal for this project was to determine from raw neural activity what the volunteer was thinking at any given time. This form of analysis have many direct applications, for example: prosthesis moving, computer games which interface with brain activity, and even piloting vehicles without need for physical interactions.
Walmart recently hosted a Kaggle competition with the aim of improving a customers' shopping experience by segmenting their store visits into different trip types. Whether they're on a last minute run for school supplies or picking up their monthly prescriptions, classifying trip types enables Walmart to create the best shopping experience for every customer.