{
  "id": 685709,
  "title": "🚀 Achieving 99.5% Accuracy on MNIST: My CNN Architecture Breakdown",
  "url": "/competitions/digit-recognizer/discussion/685709",
  "author_name": "Kemal Demirbaş",
  "post_date": "2026-03-28T20:01:49.223000",
  "votes": 0,
  "comment_count": 0,
  "views": null,
  "content": "<p>Hi everyone!</p>\n<p>I wanted to share my latest work where I achieved a 99.5% accuracy rate on the MNIST dataset, the 'Hello World' of Computer Vision.</p>\n<p>In this project, my goal wasn't just to get a high score, but also to maintain the model's generalization ability. In the CNN architecture I built:</p>\n<p>Layered convolutional structures for deep feature extraction.</p>\n<p>Strategic use of Dropout to prevent overfitting and ensure robustness.</p>\n<p>Data Augmentation techniques to enrich the dataset and improve performance.</p>\n<p>I’ve detailed the step-by-step development and error analysis within the notebook. I hope it’s helpful for those trying to break the 99.5% barrier or looking to improve their architecture.</p>\n<p>Looking forward to your feedback and questions! Link below:\n🔗 [<a href=\"https://www.kaggle.com/code/kemaldemirba/mnist-cnn-classifier-99-5-accuracy\" target=\"_blank\">https://www.kaggle.com/code/kemaldemirba/mnist-cnn-classifier-99-5-accuracy</a>]</p>",
  "messages": [
    {
      "id": 3430713,
      "postDate": "2026-03-28T20:01:49.223Z",
      "content": "<p>Hi everyone!</p>\n<p>I wanted to share my latest work where I achieved a 99.5% accuracy rate on the MNIST dataset, the 'Hello World' of Computer Vision.</p>\n<p>In this project, my goal wasn't just to get a high score, but also to maintain the model's generalization ability. In the CNN architecture I built:</p>\n<p>Layered convolutional structures for deep feature extraction.</p>\n<p>Strategic use of Dropout to prevent overfitting and ensure robustness.</p>\n<p>Data Augmentation techniques to enrich the dataset and improve performance.</p>\n<p>I’ve detailed the step-by-step development and error analysis within the notebook. I hope it’s helpful for those trying to break the 99.5% barrier or looking to improve their architecture.</p>\n<p>Looking forward to your feedback and questions! Link below:\n🔗 [<a href=\"https://www.kaggle.com/code/kemaldemirba/mnist-cnn-classifier-99-5-accuracy\" target=\"_blank\">https://www.kaggle.com/code/kemaldemirba/mnist-cnn-classifier-99-5-accuracy</a>]</p>",
      "rawMarkdown": "Hi everyone!\n\nI wanted to share my latest work where I achieved a 99.5% accuracy rate on the MNIST dataset, the 'Hello World' of Computer Vision.\n\nIn this project, my goal wasn't just to get a high score, but also to maintain the model's generalization ability. In the CNN architecture I built:\n\nLayered convolutional structures for deep feature extraction.\n\nStrategic use of Dropout to prevent overfitting and ensure robustness.\n\nData Augmentation techniques to enrich the dataset and improve performance.\n\nI’ve detailed the step-by-step development and error analysis within the notebook. I hope it’s helpful for those trying to break the 99.5% barrier or looking to improve their architecture.\n\nLooking forward to your feedback and questions! Link below:\n🔗 [https://www.kaggle.com/code/kemaldemirba/mnist-cnn-classifier-99-5-accuracy]"
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3430713": "Hi everyone!\n\nI wanted to share my latest work where I achieved a 99.5% accuracy rate on the MNIST dataset, the 'Hello World' of Computer Vision.\n\nIn this project, my goal wasn't just to get a high score, but also to maintain the model's generalization ability. In the CNN architecture I built:\n\nLayered convolutional structures for deep feature extraction.\n\nStrategic use of Dropout to prevent overfitting and ensure robustness.\n\nData Augmentation techniques to enrich the dataset and improve performance.\n\nI’ve detailed the step-by-step development and error analysis within the notebook. I hope it’s helpful for those trying to break the 99.5% barrier or looking to improve their architecture.\n\nLooking forward to your feedback and questions! Link below:\n🔗 [https://www.kaggle.com/code/kemaldemirba/mnist-cnn-classifier-99-5-accuracy]"
  }
}