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 ...

The full dataset contained 2,523 compounds and included compounds with both senolytic and non-senolytic properties so as not to bias the machine-learning algorithm.

To generate large and highly detailed forest maps, the researchers trained a type of machine learning algorithm called a deep neural network using images of the tree canopy and other sensor data ...

As big cloud players roll out machine learning tools to developers ... Also, a lot of algorithms are tree-based, mimicking a human being when we have to make a judgment — for example, we ...

Los Alamos National Laboratory scientists are front and center in using machine learning algorithms (an application ... atomic masses of the entire nuclide chart — the combination of all ...

Learn more Machine ... regression algorithms, including linear, polynomial, logarithmic, and exponential. You can also configure the chart to display the parameters of your machine learning ...

Applying machine learning algorithms and libraries: Standard implementations of machine learning algorithms are available through libraries, packages, and APIs (such as scikit-learn, Theano, Spark ...

Scientists are using machine learning algorithms to successfully model the atomic masses of the entire nuclide chart -- the combination of all possible protons and neutrons that defines elements ...