From deepfakes to natural language processing and more, the open source world is ripe with projects to support software development on the frontiers of artificial intelligence and machine learning.

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

The proliferation of open-source and proprietary software has revolutionized development, enabling rapid innovation and ...

Machine learning, concluded: Did the “no-code” tools beat manual analysis? In the finale of our experiment, we look at how the low/no-code tools performed.

Google has opened up the source code of two machine learning (ML) on-device systems, MobileNetV3 and MobileNetEdgeTPU, to the open source community.

Artificial intelligence and machine learning bring new vulnerabilities along with their benefits. Here's how experts minimized their risk.

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.

Github pulled data on the top AI repositories on-platform. The most popular programming language was Python, and TensorFlow topped the list of projects.

It has an average 3.9 rating (scale 0-5) from 87 developers who reviewed it. Tabnine AI Autocomplete for Javascript, Python, Typescript, PHP, Go, Java, Ruby & more This AI code assistant, with some 5 ...