Tweaking machine learning algorithms and models won't always be for experts only, thanks to these cutting-edge projects ...

The first step to a successful ML project is to understand that these projects require different processes, terminology, workflows, and tools than those needed by traditional development.

Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model ...

Using PyTorch to streamline machine-learning projects A platform that lets surgeons browse videos of past operations has found a way to make its machine learning more effective.

Mention machine learning, and many common frameworks pop into mind, from “old” stalwarts like Scikit-learn to juggernauts like Google’s TensorFlow. But the field is large and diverse, and ...