{
  "fetched_at": "2026-10-19T07:20:20Z",
  "source_url": "https://www.kaggle.com/competitions/fall-2023-cisc6000-deep-learning-hw2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "# Task One\n\n1. cifar.train.npy 48000 pic in numpy array. 3 channels, 32 pixels by 32 pixels\n\n2. cifar.trainlabel 48000 related labels\n\n3. cifar.test.npy 12000 testing pics\n\n4. sample_submission.csv: This is an example that YOU should generate from the model.\n\n# Task Two\n\n1. train_clean_data.zip 30000 pics cleaned image data for training\n\n2. train_noise_data.zip 30000 pics noise image data for training\n\n3. test_clean_data.zip 20000 pics cleaned image data for testing\n\n4. test_noise_data.zip 20000 pics noise image data for testing\n\n5. labels.csv: 20K labels for images in (III) and (IV)\n \n6. M.weights.hdf5 Q2 question pre-train weight.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "Generally speaking, this is a 10-classification problem and more difficult!\n\nAutoencoder\n\nIt needs a total of 3 hours running maybe!\n\n- In Kaggle:\n\n  Submit your CSV file of Q1(e) only (not table 1, just label of test dataset like sample_submission.csv)\n\n  Download your latest version of all code in notebook format including Q1 and Q2\n\n- In Blackboard:\n\n  Submit your table 1 and table 2, some graphs, and some answers in a pdf file.\n\n  Submit your all code in notebook format including Q1 and Q2",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "abstract",
      "content": "- Convolutional Neural Network",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}