Machine-learning algorithms are responsible for the vast majority of the artificial intelligence advancements and applications you hear about. (For more background, check out our first flowchart ...

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models. Topics Spotlight: New Thinking about Cloud Computing ...

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 ...

TensorFlow was originally a deep learning research project of the Google Brain Team that has since become–by way of collaboration with 50 teams at Google–an open source library deployed across ...

Deep machine learning has been around for some time, but did not work well because of limited computing power and immature algorithms. The difference between deep learning and regular machine learning ...

Within machine learning are neural networks inspired by the brain, and then deep learning. Deep learning algorithms have a particular architecture with many layers that flow through the network.