Deep learning is a hot topic and many companies feel they need to get started or risk getting left behind. Here are the five main steps on setting up a deep learning workflow.

It may be the buzziest tech trend of the moment, but machine learning is no easy matter. Before you jump into writing machine learning algorithms, here are the basics you need to start a project.

I like to divide my machine learning education into two eras: I spent the first era learning how to build models with tools like scikit-learn and TensorFlow, which was hard and took forever. I ...

Learn how to build and deploy a machine-learning data model in a Java-based production environment using Weka, Docker, and REST.

Discover the ultimate roadmap to mastering machine learning skills in 2025. Learn Python, deep learning, and more to boost ...

Next-generation ML tools visualize what data scientists see when they are building a machine learning model, reinventing the process of model building.

“Machine learning is a tool and like most tools, it works best when used properly,” said Matei Zaharia, who is the chief technologist and co-founder of Databricks.

What is deep learning? Deep learning is machine learning on steroids: it uses a technique that gives machines an enhanced ability to find—and amplify—even the smallest patterns.

Rather than changing the subject, communications leaders should aim to pinpoint who is listening and their needs and tailor established messaging to these demands.

The key distinction between traditional approaches and machine learning is that in machine learning, a model learns from examples rather than being programmed with rules.