{
  "fetched_at": "2026-10-19T21:51:59Z",
  "source_url": "https://www.kaggle.com/competitions/2019sc403/data",
  "pages": [
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "<p>Each folder contains images of size 28x28 on gray scale with folder name indicating the class to which the image belongs</p>",
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    {
      "name": "Description",
      "content": "<p>In this assignment you need to implement CNN using Keras on MNIST dataset to classify images. The implementation should be done only in python\n\nThe data consists of 7500 images belonging to 10 different classes. Train your model on this dataset. Download the test set and upload the predictions of the test set on Kaggle to measure the accuracy of your model.</p>\n",
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    },
    {
      "name": "Evaluation",
      "content": "<hr>\n<p>The evaluation metric for this competition is <a href=\"https://developers.google.com/machine-learning/crash-course/classification/accuracy\"> Accuracy </a> as defined here. \n</p><h2>Submission Format</h2>\n<p><strong>Download the test cases and run your code on it. Then create a csv file containing the filename,as in the test cases, in First column named \"ID\" and its class in Second column named \"Category\". In case of any confusion check the sample submission file</strong></p><strong>\n<p>The file should contain a header and have the following format:</p>\n<pre>Id,Category<br>image9774,0<br></pre></strong>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
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    }
  ]
}