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 ...

When deploying large-scale deep learning applications, C++ may be a better choice than Python to meet application demands or to optimize model performance. Therefore, I specifically document my recent ...

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 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 ...

Since announcements late last year about Google open-sourcing TensorFlow, the company’s open-source library for machine learning, and previous coverage at InfoQ, the data-science community has ...

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

TensorFlow not only makes it possible for developers to include the spoils of deep learning into their products, but it makes devices and software significantly more intelligent and easier to use.

Xiaoyi Lu from Ohio State University gave this talk at the 2019 OpenFabrics Workshop in Austin. "Google's TensorFlow is one of the most popular Deep Learning (DL) frameworks. We propose a unified way ...