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 42 articles )

Showing results 1 to 20 of 42.
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  1. Cox, Marco; van de Laar, Thijs; de Vries, Bert: A factor graph approach to automated design of Bayesian signal processing algorithms (2019)
  2. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  3. Aggarwal, Charu C.: Neural networks and deep learning. A textbook (2018)
  4. Albert Zeyer, Tamer Alkhouli, Hermann Ney: RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition (2018) arXiv
  5. 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
  6. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  7. Birk, Lothar; McCulloch, T. Luke: Robust generation of constrained B-spline curves based on automatic differentiation and fairness optimization (2018)
  8. Daniel Emaasit: Pymc-learn: Practical Probabilistic Machine Learning in Python (2018) arXiv
  9. 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
  10. 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
  11. Hubara, Itay; Courbariaux, Matthieu; Soudry, Daniel; El-Yaniv, Ran; Bengio, Yoshua: Quantized neural networks: training neural networks with low precision weights and activations (2018)
  12. Innocenti, Luca; Banchi, Leonardo; Bose, Sougato; Ferraro, Alessandro; Paternostro, Mauro: Approximate supervised learning of quantum gates via ancillary qubits (2018)
  13. Konate, Arouna; Du, Ruiying: Sentiment analysis of code-mixed Bambara-French social media text using deep learning techniques (2018)
  14. Lee, Seunghye; Ha, Jingwan; Zokhirova, Mehriniso; Moon, Hyeonjoon; Lee, Jaehong: Background information of deep learning for structural engineering (2018)
  15. Pandey, Ram Krishna; Ramakrishnan, A. G.: Efficient document-image super-resolution using convolutional neural network (2018)
  16. Sanyal, Amartya; Kumar, Pawan; Kar, Purushottam; Chawla, Sanjay; Sebastiani, Fabrizio: Optimizing non-decomposable measures with deep networks (2018)
  17. Schmitz, Morgan A.; Heitz, Matthieu; Bonneel, Nicolas; Ngolè, Fred; Coeurjolly, David; Cuturi, Marco; Peyré, Gabriel; Starck, Jean-Luc: Wasserstein dictionary learning: optimal transport-based unsupervised nonlinear dictionary learning (2018)
  18. Schreiber, Jacob: pomegranate: fast and flexible probabilistic modeling in Python (2018)
  19. Srajer, Filip; Kukelova, Zuzana; Fitzgibbon, Andrew: A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning (2018)
  20. Ueltzhöffer, Kai: Deep active inference (2018)

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