TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Francesco Giannini, Vincenzo Laveglia, Alessandro Rossi, Dario Zanca, Andrea Zugarini: Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow (2017) arXiv
- Paul Springer, Tong Su, Paolo Bientinesi: HPTT: A High-Performance Tensor Transposition C++ Library (2017) arXiv
- Alexander G. Anderson, Cory P. Berg, Daniel P. Mossing, Bruno A. Olshausen: DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies (2016) arXiv
- Matthew Moskewicz, Forrest Iandola, Kurt Keutzer: Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms (2016) arXiv
- Steven Eliuk, Cameron Upright, Hars Vardhan, Stephen Walsh, Trevor Gale: dMath: Distributed Linear Algebra for DL (2016) arXiv