Machine learning and deep learning are both core technologies of artificial intelligence. Yet there are key differences between them.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Today, machine learning is a widely used term that encompasses many types of programs that you’ll run across in big data analytics and data mining. At the end of the day, the “brains” actually ...

Deep learning, a subset of machine learning, refers to machine learning that takes place on artificial intelligence neural networks.

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

Machine learning, a branch of artificial intelligence, allows a computer to teach itself how to solve problems by analyzing large sets of data.

Machine learning algorithms are at the core of smartphones and online services like ChatGPT and YouTube. Here's how the technology works.

Generative models – Using deep learning algorithms and pre-existing data, generic models produce new content. These technologies are used in applications like text, video, and image production.

Gary Illyes of Google tells us Google may use machine learning to aggregate signals together for better search quality, and with RankBrain.

Leaving out neural networks and deep learning, which require a much higher level of computing resources, the most common algorithms are Naive Bayes, Decision Tree, Logistic Regression, K-Nearest ...