TensorFlow is an open source software library developed by Google for numerical computation with data flow graphs. This TensorFlow guide covers why the library matters, how to use it and more.

Google has just moved to a production release of TensorFlow Serving, its open source library for serving machine-learned models in production environments. A beta version of the technology was ...

Moreover, developers using standard TensorFlow mechanisms shouldn’t have to change their model architectures, training procedures, or processes. It follows hot on the heels of TensorFlow 2.0 ...

Google today released TensorFlow Constrained Optimization, a library aimed at addressing issues related to measuring multiple metrics and fairness.

Google today launched TensorFlow Quantum (TFQ), an open-source library for prototyping quantum machine learning models. Quantum computers aren’t quite mainstream yet, but when they arrive they ...

At version r1.5, Google's open source machine learning and neural network library is more capable, more mature, and easier to learn and use If you looked at TensorFlow as a deep learning framework ...

Dave Burke, VP of engineering at Google, announced a new version of Tensorflow optimised for mobile phones. This new library, called Tensorflow Lite, would enable developers to run their ...

Google released a TensorFlow.Text, a new text-processing library for their TensorFlow deep-learning platform. The library allows several common text pre-processing activities, such as tokenization ...

The TensorFlow machine learning framework that Google Inc. released under an Apache open-source license in late 2015 just gained an important endorsement: IBM Corp. said today that it will support ...

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.