Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features tight integration with numpy, transparent use of a GPU, efficient symbolic differentiation, speed and stability optimizations, dynamic C code generation, and extensive unit-testing and self-verification. Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal). (Source: http://freecode.com/)


References in zbMATH (referenced in 51 articles )

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  1. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  2. Katrutsa, Alexandr; Daulbaev, Talgat; Oseledets, Ivan: Black-box learning of multigrid parameters (2020)
  3. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  4. Arnaudon, Alexis; Holm, Darryl D.; Sommer, Stefan: A geometric framework for stochastic shape analysis (2019)
  5. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  6. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  7. Kühnel, Line; Sommer, Stefan; Arnaudon, Alexis: Differential geometry and stochastic dynamics with deep learning numerics (2019)
  8. Livezey, Jesse A.; Bujan, Alejandro F.; Sommer, Friedrich T.: Learning overcomplete, low coherence dictionaries with linear inference (2019)
  9. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  10. Wang, Bao; Yin, Penghang; Bertozzi, Andrea Louise; Brantingham, P. Jeffrey; Osher, Stanley Joel; Xin, Jack: Deep learning for real-time crime forecasting and its ternarization (2019)
  11. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  12. Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
  13. Andrew Beers; James Brown; Ken Chang; Katharina Hoebel; Elizabeth Gerstner; Bruce Rosen; Jayashree Kalpathy-Cramer: DeepNeuro: an open-source deep learning toolbox for neuroimaging (2018) arXiv
  14. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  15. Birk, Lothar; McCulloch, T. Luke: Robust generation of constrained B-spline curves based on automatic differentiation and fairness optimization (2018)
  16. Daniel Emaasit: Pymc-learn: Practical Probabilistic Machine Learning in Python (2018) arXiv
  17. Dan Moldovan, James M Decker, Fei Wang, Andrew A Johnson, Brian K Lee, Zachary Nado, D Sculley, Tiark Rompf, Alexander B Wiltschko: AutoGraph: Imperative-style Coding with Graph-based Performance (2018) arXiv
  18. Hananel Hazan, Daniel J. Saunders, Hassaan Khan, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma: BindsNET: A machine learning-oriented spiking neural networks library in Python (2018) arXiv
  19. Hubara, Itay; Courbariaux, Matthieu; Soudry, Daniel; El-Yaniv, Ran; Bengio, Yoshua: Quantized neural networks: training neural networks with low precision weights and activations (2018)
  20. Innocenti, Luca; Banchi, Leonardo; Bose, Sougato; Ferraro, Alessandro; Paternostro, Mauro: Approximate supervised learning of quantum gates via ancillary qubits (2018)

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