TensorFlow 2.0, released in 2019, introduced improved usability, eager execution, and tighter integration with Keras, making it more accessible for AI researchers and developers.

Keras proper, a high-level front end for building neural network models, ships with support for three back-end deep learning frameworks: TensorFlow, CNTK, and Theano.

TensorFlow 2.0 comes with a number of changes made in an attempt to improve ease of use, such as the elimination of some APIs thought to be redundant and a tight integration and reliance on tf ...

The launch of TensorFlow Quantum comes the same week as TensorFlow Dev Summit, an annual meeting of machine learning practitioners who use the framework at Google offices in Silicon Valley.

In steps TonY, LinkedIn's framework to natively run TensorFlow on Hadoop. The core idea is to run TensorFlow jobs as reliably and flexibly as other first-class citizens on Hadoop including ...