{
  "fetched_at": "2026-10-19T08:45:44Z",
  "source_url": "https://www.kaggle.com/competitions/cisc6000-deep-learning-hw1/data",
  "pages": [
    {
      "name": "data-description",
      "content": "mnist.test.npy : Testing images in numpy array.\n\nmnist.train.npy : Trainning images in numpy array.\n\nmnist.trainlabel.npy : The labels of trainning images in numpy array.\n\nsample_submission.csv : Your submission format should like this, first columns is the test image id, and the second column is the category of your prediction.",
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      "postTitle": "",
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    {
      "name": "Description",
      "content": "# Image Classification, multiclass.\n\n\nIn Kaggle:\n\n1. Submit your csv file of Q3(c) only\n\n2. Download your latest version of all code as notebook format including Q2 and Q3\n\n3. Download your all csv files including Q2 and Q3 in output folder\n\nIn Blackboard:\n\n1. Submit your answer of Q1 in pdf file.\n\n2. Q2:\n\n  (1) submit a csv file of test data prediction\n\n  (2) submit your all code as notebook format\n\n3. Q3:\n\n  (1) submit two csv files of test data prediction\n\n  (2) submit your all code as notebook format\n\n  (3) submit table 1 in pdf file",
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      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "hasIsPublished": false,
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      "hasPostTitle": false,
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}