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 43 articles )

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  1. Arridge, Simon; Maass, Peter; Öktem, Ozan; Schönlieb, Carola-Bibiane: Solving inverse problems using data-driven models (2019)
  2. Balakrishnan, Harikrishnan Nellippallil; Kathpalia, Aditi; Saha, Snehanshu; Nagaraj, Nithin: Chaosnet: a chaos based artificial neural network architecture for classification (2019)
  3. Daniel Smilkov, Nikhil Thorat, Yannick Assogba, Ann Yuan, Nick Kreeger, Ping Yu, Kangyi Zhang, Shanqing Cai, Eric Nielsen, David Soergel, Stan Bileschi, Michael Terry, Charles Nicholson, Sandeep N. Gupta, Sarah Sirajuddin, D. Sculley, Rajat Monga, Greg Corrado, Fernanda B. Viegas, Martin Wattenberg: TensorFlow.js: Machine Learning for the Web and Beyond (2019) arXiv
  4. Fan, Yuwei; Feliu-Fabà, Jordi; Lin, Lin; Ying, Lexing; Zepeda-Núñez, Leonardo: A multiscale neural network based on hierarchical nested bases (2019)
  5. Ghatak, Abhijit: Deep learning with R (2019)
  6. Higham, Catherine F.; Higham, Desmond J.: Deep learning: an introduction for applied mathematicians (2019)
  7. Jonas Fassbender: libconform v0.1.0: a Python library for conformal prediction (2019) arXiv
  8. Khoo, Yuehaw; Ying, Lexing: SwitchNet: a neural Network Model for forward and inverse scattering problems (2019)
  9. Laloy, Eric; Jacques, Diederik: Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks (2019)
  10. Prasse, Paul; Knaebel, René; Machlica, Lukáš; Pevný, Tomáš; Scheffer, Tobias: Joint detection of malicious domains and infected clients (2019)
  11. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  12. Sosnovik, Ivan; Oseledets, Ivan: Neural networks for topology optimization (2019)
  13. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  14. Tahir, Muhammad; Tayara, Hilal; Chong, Kil To: iRNA-PseKNC(2methyl): identify RNA 2’-O-methylation sites by convolution neural network and Chou’s pseudo components (2019)
  15. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  16. Xiaomeng Dong, Junpyo Hong, Hsi-Ming Chang, Michael Potter, Aritra Chowdhury, Purujit Bahl, Vivek Soni, Yun-Chan Tsai, Rajesh Tamada, Gaurav Kumar, Caroline Favart, V. Ratna Saripalli, Gopal Avinash: FastEstimator: A Deep Learning Library for Fast Prototyping and Productization (2019) arXiv
  17. Zhou, Joey Tianyi; Pan, Sinno Jialin; Tsang, Ivor W.: A deep learning framework for hybrid heterogeneous transfer learning (2019)
  18. Adrian Bevan, Thomas Charman, Jonathan Hays: HIPSTER - A python package for particle physics analyses (2018) arXiv
  19. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  20. Alex A. Alemi, Francois Chollet, Niklas Een, Geoffrey Irving, Christian Szegedy, Josef Urban: DeepMath - Deep Sequence Models for Premise Selection (2018) arXiv

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