TensorFlow core TensorFlow 2.0 focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs, and flexible model building on any platform.

Higher layers of TensorFlow and the primary TensorFlow API are implemented in Python. In addition to Python, there are APIs in C++, Java, and Go.

The US Defense Department has turned to Google for help analyzing its drone surveillance footage. Specifically, Google is working on a pilot project with the DoD which leverages TensorFlow APIs to ...

Google introduced the latest version of TensorFlow today with an emphasis on ease of use and a reduction in APIs for the machine learning framework.

It’s more production-ready than ever: TensorFlow 1.0 promises Python API stability (details here), making it easier to pick up new features without worrying about breaking your existing code.

TensorFlow Lite (TFLite) was announced in 2017 and Google is now calling it “LiteRT” to reflect how it supports third-party models. TensorFlow Lite for mobile on-device AI has “grown beyond ...

Google have also included a Keras compatibility API, allowing TensorFlow to work with this high level neural networks library.

TensorFlow is optimized with a set of distributed TensorFlow APIs that enables an expert to write native code in C++ to distribute and coordinate the workload across a systems bus and ...

Google today unveiled TensorFlow Quantum for training hybrid quantum-classical algorithms by combining TensorFlow with open source quantum library Cirq.

Google has revealed it is bringing together its machine learning and quantum computing initiatives with the launch of TensorFlow Quantum. The machine learning framework has the ability to ...