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 184 articles )

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  1. Alexander M. Rush: Torch-Struct: Deep Structured Prediction Library (2020) arXiv
  2. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  3. Benedek Rozemberczki, Oliver Kiss, Rik Sarkar: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (2020) arXiv
  4. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  5. Boso, Francesca; Tartakovsky, Daniel M.: Data-informed method of distributions for hyperbolic conservation laws (2020)
  6. Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather: ProGraML: Graph-based Deep Learning for Program Optimization and Analysis (2020) arXiv
  7. Davis, Damek; Drusvyatskiy, Dmitriy; Kakade, Sham; Lee, Jason D.: Stochastic subgradient method converges on tame functions (2020)
  8. Edward Ayers, Francisco Eiras, Majd Hawasly, Iain Whiteside: PaRoT: A Practical Framework for Robust Deep NeuralNetwork Training (2020) arXiv
  9. 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
  10. 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
  11. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)
  12. Kim, Junhyuk; Lee, Changhoon: Prediction of turbulent heat transfer using convolutional neural networks (2020)
  13. Linda, G. Merlin; Themozhi, G.; Bandi, Sudheer Reddy: Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network (2020)
  14. Liu, Peng; Song, Yan: Segmentation of sonar imagery using convolutional neural networks and Markov random field (2020)
  15. Lukas Geiger; Plumerai Team: Larq: An Open-Source Library for Training Binarized Neural Networks (2020) not zbMATH
  16. Muammar El Khatib, Wibe A de Jong: ML4Chem: A Machine Learning Package for Chemistry and Materials Science (2020) arXiv
  17. Nguyen-Thanh, Vien Minh; Zhuang, Xiaoying; Rabczuk, Timon: A deep energy method for finite deformation hyperelasticity (2020)
  18. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)
  19. Zhang, Dongkun; Guo, Ling; Karniadakis, George Em: Learning in modal space: solving time-dependent stochastic PDEs using physics-informed neural networks (2020)
  20. Alber, Maximilian; Lapuschkin, Sebastian; Seegerer, Philipp; Hägele, Miriam; Schütt, Kristof T.; Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert; Dähne, Sven; Kindermans, Pieter-Jan: iNNvestigate neural networks! (2019)

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