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

Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

AI’s biggest constraint isn’t algorithms anymore. It’s data…specifically, high-quality, forward-looking data. It is the “Rare ...

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

Artificial intelligence has become a popular tool for job recruiters, in part because programmers can code applicant-screening algorithms to avoid any explicit discrimination in their decision-making ...

We developed two algorithms to identify patients with stomach, lung, colorectal, breast, and cervical cancers: diagnosis only (algorithm 1), and combining diagnosis, treatments, and medicines ...

Through data, algorithms communicate with their environments and get to “know about” and “learn from” what is happening around them. Algorithms without living data are no more than sheer mathematical ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...