Previous columns in this series introduced the problem of data protection in machine learning (ML), emphasizing the real challenge that operational query data pose. That is, when you use an ML system, ...

Machine learning is often key to success for today’s institutions that rely heavily on data. But often, data science teams can have a difficult time convincing their organizations of the breadth and ...

Gengo, a leader in expert, high-scale crowdsourced translation services, is taking aim at the growing need for high-quality multilingual data to train tomorrow’s advanced AI (artificial intelligence) ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

One of the big challenges of developing a machine learning project can be simply getting enough relevant data to train the algorithms. That’s where Superb AI, a member of the Y Combinator Winter 2019 ...

TLDR: The Complete 2020 Big Data and Machine Learning Bundle breaks down understanding and getting started in two of the tech era’s biggest new growth sectors. It’s instructive to know just how big ...

eWeek content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More Machine learning (ML) uses advanced mathematical models ...

Mathematicians have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the tens or hundreds of thousands of images used by typical machine learning ...

AI and ML are the driving forces behind various industries across the globe. The Professional Certificate course of Purdue ...

Value stream management involves people in the organization to examine workflows and other processes to ensure they are deriving the maximum value from their efforts while eliminating waste — of ...