CNTK

The Microsoft Cognitive Toolkit (https://cntk.ai), is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK has been available under an open-source license since April 2015. It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code.


References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Guo, Jian; He, He; He, Tong; Lausen, Leonard; Li, Mu; Lin, Haibin; Shi, Xingjian; Wang, Chenguang; Xie, Junyuan; Zha, Sheng; Zhang, Aston; Zhang, Hang; Zhang, Zhi; Zhang, Zhongyue; Zheng, Shuai; Zhu, Yi: GluonCV and GluonNLP: deep learning in computer vision and natural language processing (2020)
  2. Szymański, Piotr; Kajdanowicz, Tomasz: scikit-multilearn: a scikit-based Python environment for performing multi-label classification (2019)
  3. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  4. 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
  5. Zhao-Yun Chen, Cheng Xue, Si-Ming Chen, Guo-Ping Guo: VQNet: Library for a Quantum-Classical Hybrid Neural Network (2019) arXiv
  6. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  7. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
  8. Oleksii Kuchaiev; Boris Ginsburg; Igor Gitman; Vitaly Lavrukhin; Carl Case; Paulius Micikevicius: OpenSeq2Seq: extensible toolkit for distributed and mixed precision training of sequence-to-sequence models (2018) arXiv
  9. Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo: TensorLayer: A Versatile Library for Efficient Deep Learning Development (2017) arXiv