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Training MNIST benchmark with PyTorch

Xinyu Liu


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  <dc:creator>Xinyu Liu</dc:creator>
  <dc:date>2020-09-09</dc:date>
  <dc:description>This notebook reproduces a simple benchmark experiment that trains a convolutional neural network based on the MNIST dataset. In particular, the neural network is built and trained with PyTorch.

MNIST is a database with 60,000 training images and 10,000 testing images that contains hand-written digits. Please see http://yann.lecun.com/exdb/mnist/ for more details.</dc:description>
  <dc:identifier>https://zenodo.org/record/4344415</dc:identifier>
  <dc:identifier>10.5281/zenodo.4344415</dc:identifier>
  <dc:identifier>oai:zenodo.org:4344415</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.4344414</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/chameleon</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Training MNIST benchmark with PyTorch</dc:title>
  <dc:type>info:eu-repo/semantics/workingPaper</dc:type>
  <dc:type>publication-workingpaper</dc:type>
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