Modern machine learning systems thrive on data, but labelled data is often expensive, slow to collect, and limited in scope. This constraint has pushed researchers to explore approaches that can learn meaningful patterns without relying heavily on human annotation. Self-supervised learning has emerged as a powerful solution to this challenge. By generating supervisory signals directly from raw data, models can learn useful representations that transfer effectively to downstream tasks. This approach has become a cornerstone of pre-training strategies across natural language processing, computer vision, and speech recognition, and it...
Modern machine learning systems thrive on data, but labelled data is often expensive, slow to collect, and limited in scope. This constraint has pushed...
Imagine watching a bustling city street from a high balcony. People walk, pause, turn back, rush ahead, or linger at storefronts. Each movement tells...
Every organisation carries a silent compass that guides decisions, fuels conversations and shapes strategies. This compass is not a dashboard or a report. It...
Introduction
Zillow has changed the real estate market in a big way in today's digital-first era. Zillow makes it easy for buyers, renters, landlords, and...
