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.

Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you.

TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback. The result is a machine learning framework that is easier to work with—for example, by ...

After uncovering a unifying algorithm that links more than 20 common machine-learning approaches, researchers organized them into a 'periodic table of machine learning' that can help scientists ...

Machine learning algorithms do a lot for us every day -- send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to ...

Machine learning is hard.Algorithms in a particular use case often either don't work or don't work well enough, leading to some serious debugging. And finding the perfect algorithm–the set of ...

Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature ...

Acknowledging the pain points of the NOVA classification system, researchers have developed a machine learning algorithm to accurately predict the degree of processing for any food. The extent to ...