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  "fetched_at": "2026-10-19T18:56:44Z",
  "source_url": "https://www.kaggle.com/competitions/deep-learning-dive-in-week-kaggle-challenge/data",
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    {
      "name": "data-description",
      "content": "The dataset consists of two files:\n**1. Training Data (train.csv):**\n- Features: Columns labeled '1x1' through '28x28'.\n- Target Labels: The 'label' column contains the class labels corresponding to each set of features.\n- Entries: The training set includes 8000 entries.\n\n**2. Testing Data (test.csv):**\n- Features: Columns labeled '1x1' through '28x28'.\n- Identification: The 'ID' column uniquely identifies each sample.\n- Entries: The test set comprises 2000 entries.\n\n**Submission Guidelines:**\n- Predict labels based on the features provided in the 'test.csv' file.\n- Update the predicted labels in the 'sample_submission.csv' file at the corresponding 'ID'.\n- Submit the modified 'sample_submission.csv' for evaluation.\n\n\nThe 'id' column is to uniquely identify data points.",
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    {
      "name": "Description",
      "content": "Hey there! Welcome to the PyTorch Party! 🎉 This competition is like a cool playground for learning about neural networks and stuff. Don't stress if your score isn't super high at first. Just keep trying, and you'll get the hang of it!\n\nAnd here's the secret sauce: make your model small and snappy. That's the key to success! Have fun coding, and you'll be a pro in no time! 👩‍💻🚀\n\nDon't forget, at the end of the competition, we'll ask you to submit your models along with your awesome code. Happy coding!\n\nAnd hey, as you strut through this coding fiesta, remember, you're going to meet our dataset again—multiple times! 🔄 It's like seeing an old friend who keeps dropping by. Embrace the data, and let's make this party unforgettable!\n\n*Happy (Deep) Learning :)*",
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    {
      "name": "Evaluation",
      "content": "Submissions will be assessed based on the following criteria:\n\n- The **f1_score** of predictions, with a focus on addressing less frequent classes in the dataset.\n- The **size of your model**, recognized as a significant element influencing the overall assessment.\n\nTo know more about f1_score refer to the following link: https://www.baeldung.com/cs/multi-class-f1-score.",
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    {
      "name": "rules",
      "content": "- **Max Team Size:** 2\n- **Deadline for submission:** 11:59 PM 17th Jan 2024.",
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    },
    {
      "name": "abstract",
      "content": "Welcome to Deep Learning Dive-in Week Kaggle Competition!!",
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