Machine learning is powering most of the recent advancements in AI, including computer vision, natural language processing, predictive analytics, autonomous systems, and a wide range of applications.

Machine learning models are mathematical representations of real-world processes that are used to make predictions, and are created by providing training data for an algorithm to learn from.

Today’s data scientists and machine learning engineers now have a wide range of choices for how they build ... algorithm on particular training data is a machine learning model.

To build the taxonomy ... to create human-centered explanations for machine-learning models. This new system will also convert algorithms designed to explain model-ready datasets into formats ...

A team from Google Research has open-sourced Model Search, an automated machine learning (AutoML ... training iterations and model parameters. By building upon previous knowledge for a given ...

This article breaks down the machine learning ... algorithm is to maximize the rating of these SERPs using only the document (and query) features. Intuitively we may want to build a model that ...

Systems controlled by next-generation computing algorithms could give rise ... to quickly adapt to a patient's heartbeat. "Big machine learning models have to consume lots of power to crunch ...