By contrast, TensorFlow supports researchers developing algorithms, as well as product teams training models and deploying them for customers at scale, all with the same code. Autodifferentiation.

The example below demonstrates the Keras data set and Sequential model APIs, running in Google Colab, which is a convenient (and free) place to run TensorFlow samples and experiments.

At the start of May, I decided to get TensorFlow Developer Certified. So I set myself up with a curriculum to sharpen my skills and took the certification — turns out, I passed.

Many developers who use Python for machine learning are now switching to PyTorch. Find out why and what the future could hold for TensorFlow.

In the spirit of open-source code, Google hopes that access and use by researchers, engineers and even hobbyists will result in even better machine learning capabilities in the future.

TensorFlow will also offer a conversion tool to update TensorFlow 1.x Python code to TensorFlow 2.0 compatible APIs. The tool will also flag cases in which code is unable to be converted ...

Google LLC today launched an enterprise version of TensorFlow, the popular open-source artificial intelligence framework it created to run machine learning, deep learning and other statistical and ...

Google Cloud debuted TensorFlow Enterprise, an optimized version of open source machine learning framework TensorFlow for large businesses.

His team at PNNL has created just such an implementation—one that uses MPI as the backbone while abstracting all the messy code changing required for deep learning analysts and HPC system operators ...

Researchers optimizing TensorFlow started by refactoring the code to leverage the Intel MKL-DNN primitives wherever possible. This enabled scalable performance on the target Intel architectures. But ...