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 498 articles )

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  1. Gahm, Christian; Uzunoglu, Aykut; Wahl, Stefan; Ganschinietz, Chantal; Tuma, Axel: Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning (2022)
  2. Petrasova, Iveta; Karban, Pavel; Kropik, Petr; Panek, David; Dolezel, Ivo: Optimization of selected operation characteristics of array antennas (2022)
  3. Stehr, Mark-Oliver; Kim, Minyoung; Talcott, Carolyn L.: A probabilistic approximate logic for neuro-symbolic learning and reasoning (2022)
  4. Adam Pocock: Tribuo: Machine Learning with Provenance in Java (2021) arXiv
  5. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  6. Amini Niaki, Sina; Haghighat, Ehsan; Campbell, Trevor; Poursartip, Anoush; Vaziri, Reza: Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (2021)
  7. Anderson, Lara B.; Gerdes, Mathis; Gray, James; Krippendorf, Sven; Raghuram, Nikhil; Ruehle, Fabian: Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning (2021)
  8. Angeli, Andrea; Desmet, Wim; Naets, Frank: Deep learning for model order reduction of multibody systems to minimal coordinates (2021)
  9. Angeli, Andrea; Desmet, Wim; Naets, Frank: Deep learning of multibody minimal coordinates for state and input estimation with Kalman filtering (2021)
  10. Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis: ADAPT : Awesome Domain Adaptation Python Toolbox (2021) arXiv
  11. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  12. Arvind U. Raghunathan, Devesh K. Jha, Diego Romeres: PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance (2021) arXiv
  13. Babu, G. Jogesh; Banks, David; Cho, Hyunsoon; Han, David; Sang, Hailin; Wang, Shouyi: A statistician teaches deep learning (2021)
  14. Bar, Leah; Sochen, Nir: Strong solutions for PDE-based tomography by unsupervised learning (2021)
  15. Basani, Jasvith Raj; Bhattacherjee, Aranya: Continuous-variable deep quantum neural networks for flexible learning of structured classical information (2021)
  16. Bertsimas, Dimitris; Stellato, Bartolomeo: The voice of optimization (2021)
  17. Bobev, Nikolay; Fischbacher, Thomas; Gautason, Fridrik Freyr; Pilch, Krzysztof: New (\mathrmAdS_4) vacua in dyonic ISO(7) gauged supergravity (2021)
  18. Bolte, Jérôme; Pauwels, Edouard: Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning (2021)
  19. Bortolato, Blaž; F. Kamenik, Jernej; Košnik, Nejc; Smolkovič, Aleks: Optimized probes of \textitCP-odd effects in the (t \barth) process at hadron colliders (2021)
  20. Burashnikova, Aleksandra; Maximov, Yury; Clausel, Marianne; Laclau, Charlotte; Iutzeler, Franck; Amini, Massih-Reza: Learning over no-preferred and preferred sequence of items for robust recommendation (2021)

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