While building machine learning models is fundamental to today’s narrow applications of AI, there are a variety of different ways to go about realizing the same ends.

The strategic advantage of QML continues to expand its presence in industries that deal with complex, high-dimensional data.

In a study published in the journal Information Systems Research, Texas Tech University's Shuo Yu and his collaborators ...

Consider a machine learning model that classifies images. If your dataset is composed of 100×100-pixel images, then your problem space has 10,000 features, one per pixel.

A machine learning model that processes text must not only compute every word but also take into consideration how words come in sequences and relate to each other.

Ensemble models, comprising multiple algorithms or simulations to improve prediction accuracy, are increasingly being applied ...

Yes, there’s the option of building your own specific models using Azure’s Machine Learning studio, working with tools like PyTorch and TensorFlow to design and train models from scratch.

Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends ...

Data poisoning can render machine learning models inaccurate, possibly resulting in poor decisions based on faulty outputs. With no easy fixes available, security pros must focus on prevention and ...