Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...

Why normalizing your clinical and claims data into standard terminologies is critical to supporting forward-thinking initiatives such as big data analytics, population health management and semantic ...

Empromptu's "golden pipeline" approach tackles the last-mile data problem in agentic AI by integrating normalization directly into the application workflow — replacing weeks of manual data prep with ...

It’s tempting just to replicate all databases in the cloud, but it’s a much better approach to get your data house in order as part of the move. Last week I discussed database normalization as a best ...

It’s time for traders to start paying attention to a data revolution underway that is increasingly impacting their ability to both scale their business and provide value to their clients. Capital ...

This article explains how to programmatically normalize numeric data for use in a machine learning (ML) system such as a deep neural network classifier or clustering algorithm. Suppose you are trying ...

We used dual embeddings for English and Bulgarian languages, encoding both syntactic and polarity information for the words. The embeddings were subsequently aligned so that they were in the same ...

Arguably, the two biggest challenges in the FAST ecosystem are managing the ad experience and delivering ROI for the brands that support the platform. Evan Shapiro, CEO, ESHAP, Patrick Courtney, SVP, ...