That library, TensorFlow, was developed by the Google Brain team over the past several years and released to open source in November 2015. TensorFlow does computation using data flow graphs.

TensorFlow core, an open source library for developing and training machine learning models; TensorFlow.js, a JavaScript library for training and deploying models in the browser and on Node.js; ...

Not just that, but he’s also done it using Commodore BASIC. TensorFlow Lite works by the model being created as a C array which is then parsed and run by an interpreter on the microcontroller.

Google today released TensorFlow Graph Neural Networks (TF-GNN) in alpha, a library designed to make it easier to work with graph structured data using TensorFlow, its machine learning framework.

There is no real middle ground when it comes to TensorFlow use cases. Most implementations take place either in a single node or at the drastic Google-scale, with few scalability stories in between.

Google today announced the launch of version 0.8 of TensorFlow, its open source library for doing the hard computation work that makes machine learning possible.