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.

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 data flow graphs TensorFlow supports machine learning, neural networks, and deep learning in the larger context of data flow graphs.

“TensorFlow is a machine learning library that’s used across Google for applying deep learning to a lot of different areas,” says Rajat Mongo, a technical lead on the TensorFlow project, in a YouTube ...

TensorFlow Hub encourages the publication and discovery of self-contained modular pieces of TensorFlow graphs for reuse across similar tasks.

Graphs: TensorFlow provides a graphical means of guiding the flow of data through a machine learning application with a dataflow graph.

While DeepMind’s original implementation uses an older TensorFlow 1.0 framework, which lacks compatibility with recent libraries, we adapt their architecture to TensorFlow 2, exploring the newly ...

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.

The search function in Google Photos uses TensorFlow. Tim Stenovec/Business Insider Google made waves Monday when it made its new artificial intelligence system TensorFlow open source.