At version r1.5, Google's open source machine learning and neural network library is more capable, more mature, and easier to learn and use If you looked at TensorFlow as a deep learning framework ...

TensorFlow seems to perform as well as anything out there for neural network and deep learning training, despite an early benchmark that falsely indicated otherwise because of differing GPU libraries.

Other optimizations to TensorFlow components resulted in significant CPU performance gains for various deep learning models. Using the Intel MKL imalloc routine, both TensorFlow and the Intel MKL-DNN ...

Google enhances TensorFlow with deep learning capabilities and parallelism techniques for developer choice in machine language tooling.

In the dynamic world of machine learning, two heavyweight frameworks often dominate the conversation: PyTorch and TensorFlow. These frameworks are more than just a means to create sophisticated ...

Developers have combined the two open-source technologies -- Kaldi and TensorFlow -- with hopes of advancing automatic speech recognition.

Researchers at North Carolina State University recently presented a paper at the International Conference on Supercomputing (ICS) on their new technique, "deep reuse" (DR), that can speed up ...

Google last week open sourced TensorFlow, a new machine learning library used in deep learning projects. Even though the Web giant has just started using the software in its products, it apparently ...

At Google’s inaugural TensorFlow Dev Summit in Mountain View, California, today, Google announced the release of version 1.0 of its TensorFlow open source framework for deep learning, a trendy ...

At a presentation during Google I/O 2019, Google announced TensorFlow Graphics, a library for building deep neural networks for unsupervised learning tasks in computer vision. The library contains ...