Many developers who use Python for machine learning are now switching to PyTorch. Find out why and what the future could hold for TensorFlow.

This is new: TensorFlow 2.18 integrates the current version 2.0 of NumPy and, with Hermetic CUDA, will no longer require local CUDA libraries during the build.

TensorFlow has become the most popular tool and framework for machine learning in a short span of time. It enjoys tremendous popularity among ML engineers and developers.

Our data science doctor provides a hands-on neural networking tutorial to explain how to get started with the popular Keras library, a high-level wrapper over TensorFlow.

For those unfamiliar, TensorFlow is Google’s incredibly powerful artificial intelligence software that powers many of Google’s services and initiatives, including AlphaGo.

Engineers working on Google’s TensorFlow machine learning framework have revealed a subproject, MLIR, that is intended to be a common intermediate language for machine learning frameworks.

It’s more production-ready than ever: TensorFlow 1.0 promises Python API stability (details here), making it easier to pick up new features without worrying about breaking your existing code.

From understanding loops, variables, and strings, to tackling advanced tools, like TensorFlow and Selenium Webdriver, this 50-hour collection will give you the training to start working with ...

Google this week has published a new version of its TensorFlow machine learning software that adds support for iOS. Google initially teased that it was working on iOS support for TensorFlow last ...