TensorFlow 2.0, released in October 2019, revamped the framework significantly based on user feedback. The result is a machine learning framework that is easier to work with—for example, by ...

Think of a tensor as a multi-dimensional array. In TensorFlow, all data is represented as tensors, which are the primary data structures that are used to represent and manipulate data in TensorFlow.

Also, TensorFlow is built to be able to distribute the processing across multiple machines and/or GPUs. ... The cross_entropy tensor will be used during training of the neutral network.

Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations.The purpose of Mesh TensorFlow is to formalize and implement ...

TensorFlow 1.x was all about building static graphs in a very un-Python manner, ... (Tensor Processing Units), which deliver unparalleled performance for training models at massive scales. ...

Don't mess up tensorflow. We provide no wrapping classes. Instead, we use a tensor itself so that developers can program freely as before with tensorflow. Don't mess up the python style. We believe ...

TensorFlow is a software library that provides a high-level interface for building, training, and deploying machine learning models. TensorFlow supports various types of models, such as neural ...

TensorFlow version 0.9 also adds a bunch of other features including Python 3.5 support, added support for processing on MacOS GPUs, and a bunch of bug fixes.