Keras: Deep Learning library for Theano and TensorFlow. Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need a deep learning library that: allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). supports both convolutional networks and recurrent networks, as well as combinations of the two. supports arbitrary connectivity schemes (including multi-input and multi-output training). runs seamlessly on CPU and GPU. Read the documentation at Keras is compatible with: Python 2.7-3.5.

References in zbMATH (referenced in 120 articles )

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  1. Bai, Tao; Tahmasebi, Pejman: Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning (2021)
  2. Canchumuni, Smith W. A.; Castro, Jose D. B.; Potratz, Júlia; Emerick, Alexandre A.; Pacheco, Marco Aurélio C.: Recent developments combining ensemble smoother and deep generative networks for facies history matching (2021)
  3. Fermanian, Adeline: Embedding and learning with signatures (2021)
  4. Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado: Fast-DENSER: Fast Deep Evolutionary Network Structured Representation (2021) not zbMATH
  5. Haghighat, Ehsan; Raissi, Maziar; Moure, Adrian; Gomez, Hector; Juanes, Ruben: A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (2021)
  6. Lye, Kjetil O.; Mishra, Siddhartha; Ray, Deep; Chandrashekar, Praveen: Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks (2021)
  7. Nikolopoulos, Konstantinos; Punia, Sushil; Schäfers, Andreas; Tsinopoulos, Christos; Vasilakis, Chrysovalantis: Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions (2021)
  8. Ranade, Rishikesh; Hill, Chris; Pathak, Jay: Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (2021)
  9. Su, Wei-Hung; Chou, Ching-Shan; Xiu, Dongbin: Deep learning of biological models from data: applications to ODE models (2021)
  10. Urbaniak, Ilona; Wolter, Marcin: Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network (2021)
  11. Vasilis Nikolaidis: The nnlib2 library and nnlib2Rcpp R package for implementing neural networks (2021) not zbMATH
  12. Vasilyeva, Maria; Tyrylgin, Aleksei; Brown, Donald L.; Mondal, Anirban: Preconditioning Markov chain Monte Carlo method for geomechanical subsidence using multiscale method and machine learning technique (2021)
  13. Vasilyeva, Maria; Tyrylgin, Aleksey: Machine learning for accelerating macroscopic parameters prediction for poroelasticity problem in stochastic media (2021)
  14. Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc P. Deisenroth, ST John: GPflux: A Library for Deep Gaussian Processes (2021) arXiv
  15. Vlassis, Nikolaos N.; Sun, WaiChing: Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (2021)
  16. Wang, Kun; Sun, WaiChing; Du, Qiang: A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks (2021)
  17. Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia: Neko: a Library for Exploring Neuromorphic Learning Rules (2021) arXiv
  18. Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi, Andrea Cavallaro: PrivEdge: From Local to Distributed Private Training and Prediction (2020) arXiv
  19. Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann: From Things’ Modeling Language (ThingML) to Things’ Machine Learning (ThingML2) (2020) arXiv
  20. Baines, W.; Kuchment, P.; Ragusa, J.: Deep learning for 2D passive source detection in presence of complex cargo (2020)

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