Keras

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.io. Keras is compatible with: Python 2.7-3.5.


References in zbMATH (referenced in 131 articles )

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  1. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  2. Bai, Tao; Tahmasebi, Pejman: Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning (2021)
  3. Boumezoued, Alexandre; Elfassihi, Amal: Mortality data correction in the absence of monthly fertility records (2021)
  4. 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)
  5. Dmitry Soshnikov, Yana Valieva: mPyPl: Python Monadic Pipeline Library for Complex Functional Data Processing (2021) arXiv
  6. Fermanian, Adeline: Embedding and learning with signatures (2021)
  7. Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado: Fast-DENSER: Fast Deep Evolutionary Network Structured Representation (2021) not zbMATH
  8. Gareth D. Simons: The cityseer Python package for pedestrian-scale network-based urban analysis (2021) arXiv
  9. 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)
  10. Kiermayer, Mark; Weiß, Christian: Grouping of contracts in insurance using neural networks (2021)
  11. Kratsios, Anastasis: The universal approximation property. Characterization, construction, representation, and existence (2021)
  12. Larios-Cárdenas, Luis Ángel; Gibou, Frederic: A deep learning approach for the computation of curvature in the level-set method (2021)
  13. 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)
  14. Mirco Ravanelli; Titouan Parcollet; et al: SpeechBrain: A General-Purpose Speech Toolkit (2021) arXiv
  15. 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)
  16. Ranade, Rishikesh; Hill, Chris; Pathak, Jay: Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (2021)
  17. Su, Wei-Hung; Chou, Ching-Shan; Xiu, Dongbin: Deep learning of biological models from data: applications to ODE models (2021)
  18. Urbaniak, Ilona; Wolter, Marcin: Quality assessment of compressed and resized medical images based on pattern recognition using a convolutional neural network (2021)
  19. Vasilis Nikolaidis: The nnlib2 library and nnlib2Rcpp R package for implementing neural networks (2021) not zbMATH
  20. 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)

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