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 206 articles )

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  1. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  2. Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi, Andrea Cavallaro: PrivEdge: From Local to Distributed Private Training and Prediction (2020) arXiv
  3. Arridge, S.; Hauptmann, A.: Networks for nonlinear diffusion problems in imaging (2020)
  4. Arun S. Maiya: ktrain: A Low-Code Library for Augmented Machine Learning (2020) arXiv
  5. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  6. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  7. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  8. Boso, Francesca; Tartakovsky, Daniel M.: Data-informed method of distributions for hyperbolic conservation laws (2020)
  9. Budninskiy, Max; Abdelaziz, Ameera; Tong, Yiying; Desbrun, Mathieu: Laplacian-optimized diffusion for semi-supervised learning (2020)
  10. Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather: ProGraML: Graph-based Deep Learning for Program Optimization and Analysis (2020) arXiv
  11. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
  12. Davis, Damek; Drusvyatskiy, Dmitriy; Kakade, Sham; Lee, Jason D.: Stochastic subgradient method converges on tame functions (2020)
  13. Dittmer, Sören; Kluth, Tobias; Maass, Peter; Otero Baguer, Daniel: Regularization by architecture: a deep prior approach for inverse problems (2020)
  14. Edward Ayers, Francisco Eiras, Majd Hawasly, Iain Whiteside: PaRoT: A Practical Framework for Robust Deep NeuralNetwork Training (2020) arXiv
  15. Feiyu Chen; David Sondak; Pavlos Protopapas; Marios Mattheakis; Shuheng Liu; Devansh Agarwal; Marco Di Giovanni: NeuroDiffEq: A Python package for solving differential equations with neural networks (2020) not zbMATH
  16. Feng, Yiwei; Liu, Tiegang; Wang, Kun: A characteristic-featured shock wave indicator for conservation laws based on training an artificial neuron (2020)
  17. Fernando Pérez-García, Rachel Sparks, Sebastien Ourselin: TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020) arXiv
  18. Frank Mancolo: Eisen: a python package for solid deep learning (2020) arXiv
  19. Heider, Yousef; Wang, Kun; Sun, WaiChing: SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (2020)
  20. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)

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