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Image classification in Galaxy with MNIST handwritten digits dataset

Kaivan Kamali


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.4697906", 
  "title": "Image classification in Galaxy with MNIST handwritten digits dataset", 
  "issued": {
    "date-parts": [
      [
        2021, 
        4, 
        16
      ]
    ]
  }, 
  "abstract": "<p>Credit:&nbsp;Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. &quot;Gradient-based learning applied to document recognition.&quot;&nbsp;Proceedings of the IEEE, 86(11):2278-2324, November 1998<br>\n<br>\nThis is a subset of MNIST handwritten digits dataset (http://yann.lecun.com/exdb/mnist/). Training data of composed of 12,000 images of digits 0 to 9. Test data is composed of 6,000 images of digits 0 to 9 (Original dataset has 60,000 training and 10,000 testing images. We are using a subset for a Galaxy tutorial, so the training is not too computationally intensive). Images are grayscale and 28 by 28 pixels. Each pixel has a value between 0 and 255 (0 for color black, 255 for color white, and all other values for different shades of gray).&nbsp; &nbsp;</p>", 
  "author": [
    {
      "family": "Kaivan Kamali"
    }
  ], 
  "note": "Credit: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. \"Gradient-based learning applied to document recognition.\" Proceedings of the IEEE, 86(11):2278-2324, November 1998\nhttp://yann.lecun.com/exdb/mnist/", 
  "type": "dataset", 
  "id": "4697906"
}
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