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 153 articles )

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  1. Petrasova, Iveta; Karban, Pavel; Kropik, Petr; Panek, David; Dolezel, Ivo: Optimization of selected operation characteristics of array antennas (2022)
  2. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  3. 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)
  4. Bai, Tao; Tahmasebi, Pejman: Efficient and data-driven prediction of water breakthrough in subsurface systems using deep long short-term memory machine learning (2021)
  5. Boumezoued, Alexandre; Elfassihi, Amal: Mortality data correction in the absence of monthly fertility records (2021)
  6. 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)
  7. Carter Lee Rhea, Julie Hlavacek-Larrondo, Laurie Rousseau-Nepton, Benjamin Vigneron, Louis-Simon Guité: LUCI: A Python package for SITELLE spectral analysis (2021) arXiv
  8. Chauhan, Sunita S.; Dargad, Sweta A.: Deep learning for analysis of COVID-19 electronic health records (2021)
  9. Chen, Heng-Yu; He, Yang-Hui; Lal, Shailesh; Majumder, Suvajit: Machine learning Lie structures & applications to physics (2021)
  10. Dmitry Soshnikov, Yana Valieva: mPyPl: Python Monadic Pipeline Library for Complex Functional Data Processing (2021) arXiv
  11. Emmanouil Krasanakis, Symeon Papadopoulos, Ioannis Kompatsiaris, Andreas Symeonidis: pygrank: A Python Package for Graph Node Ranking (2021) arXiv
  12. Fermanian, Adeline: Embedding and learning with signatures (2021)
  13. Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado: Fast-DENSER: Fast Deep Evolutionary Network Structured Representation (2021) not zbMATH
  14. Flori, Andrea; Regoli, Daniele: Revealing pairs-trading opportunities with long short-term memory networks (2021)
  15. Fossan, Fredrik E.; Müller, Lucas O.; Sturdy, Jacob; Bråten, Anders T.; Jørgensen, Arve; Wiseth, Rune; Hellevik, Leif R.: Machine learning augmented reduced-order models for FFR-prediction (2021)
  16. Gareth D. Simons: The cityseer Python package for pedestrian-scale network-based urban analysis (2021) arXiv
  17. Haghighat, Ehsan; Bekar, Ali Can; Madenci, Erdogan; Juanes, Ruben: A nonlocal physics-informed deep learning framework using the peridynamic differential operator (2021)
  18. 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)
  19. Heavlin, William D.: On ensembles, I-optimality, and active learning (2021)
  20. Kiermayer, Mark; Weiß, Christian: Grouping of contracts in insurance using neural networks (2021)

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