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      WekaDeeplearning4j: A deep learning package for weka based on Deeplearning4j

      Lang, Steven; Bravo-Marquez, Felipe; Beckham, Christopher J.; Hall, Mark A.; Frank, Eibe
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      WDL4J_KBS2019.pdf
      Accepted version, 396.6Kb
      DOI
       10.1016/j.knosys.2019.04.013
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      Lang, S., Bravo-Marquez, F., Beckham, C. J., Hall, M. A., & Frank, E. (2019). WekaDeeplearning4j: A deep learning package for weka based on Deeplearning4j. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2019.04.013
      Permanent Research Commons link: https://hdl.handle.net/10289/12604
      Abstract
      Deep learning is a branch of machine learning that generates multi-layered representations of data, commonly using artificial neural networks, and has improved the state-of-the-art in various machine learning tasks (e.g., image classification, object detection, speech recognition, and document classification). However, most popular deep learning frameworks such as TensorFlow and PyTorch require users to write code to apply deep learning. We present WekaDeeplearning4j, a Weka package that makes deep learning accessible through a graphical user interface (GUI). The package uses Deeplearning4j as its backend, provides GPU support, and enables GUI-based training of deep neural networks such as convolutional and recurrent neural networks. It also provides pre-processing functionality for image and text data.
      Date
      2019
      Type
      Journal Article
      Publisher
      Elsevier BV
      Rights
      This is an author’s accepted version of an article published in the journal: Knowledge-Based Systems. © 2019 Elsevier.
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      • Computing and Mathematical Sciences Papers [1436]
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