{
  "fetched_at": "2026-10-19T07:20:17Z",
  "source_url": "https://www.kaggle.com/competitions/fordham-deep-learning-hw2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<h2>Task One</h2>\n\n<p>1. cifar.train.npy 48000 pic in numpy array. 3 channel , 32 pixels by 32 pixels</p>\n\n<p>2. cifar.trainlabel 48000 related labels</p>\n\n<p>3. cifar.test.npy 12000 testing pics</p>\n\n<p>4. sample_submission.csv: this is an example that YOU should generate from the model.</p>\n\n<h2>Task Two</h2>\n\n<p>1. train_clean_data.zip 30000 pics cleaned image data for training</p>\n\n<p>2. train_noise_data.zip 30000 pics noise image data for training</p>\n\n<p>3. test_clean_data.zip 20000 pics cleaned image data for testing</p>\n\n<p>4. test_noise_data.zip 20000 pics noise image data for testing</p>\n\n<p>5. labels.csv: 20K labels for images in (III) and (IV)</p>\n\n<p>6. M.weights.hdf5 Q2 question pre-train weight.</p>",
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    {
      "name": "Description",
      "content": "<p>General speaking this is a 10 classification problems and more difficult!</p>\n<p>Autoencoder</p>\n<p>It needs total 3 hours running maybe!</p>\n\n<p>In Kaggle: </p>\n<p>1. Submit your csv file of Q1(e) only (not table 1, just label of test dataset like sample_submission.csv) </p>\n<p>2. Download your latest version of all code as notebook format including Q1 and Q2</p>\n\n\n<p>In Blackboard: </p>\n<p>1. Submit your table 1 and table 2, some graphs and some answers in pdf file. </p>\n<p>2. Submit your all code as notebook format including Q1 and Q2</p>\n\n",
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    },
    {
      "name": "Timeline",
      "content": "<p>Due：Oct/27th 2021</p>",
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    {
      "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!",
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}