{
  "schema": "ml-prep/item@1",
  "item": {
    "id": "mit-6-036-s2018-final-q9",
    "area": "machine-learning",
    "topic": "convolution",
    "origin": "external",
    "title": "Convolutional representation and detector design",
    "skills": [
      "convolution-operation",
      "output-shape",
      "receptive-field"
    ],
    "priority": "core",
    "difficulty": "medium",
    "estimated_minutes": 18,
    "prerequisites": [
      "matrix-indexing",
      "relu"
    ],
    "source_id": "mit-6-036-exams",
    "locator": {
      "document": "Spring 2018 Final Exam",
      "label": "Question 9: Convoluted network",
      "page_label": "problem PDF pp. 23–24",
      "solution_pages": "23–24"
    },
    "links": {
      "problem": "https://introml.mit.edu/_static/spring23/final/review/Spring18-final.pdf#page=23",
      "solution": "https://introml.mit.edu/_static/spring23/final/review/Spring18-final-solutions.pdf#page=23"
    },
    "selection_note": "Requires an actual convolution and composes small filters into a deeper detector instead of testing shape recall alone.",
    "answer_status": "official-solution"
  }
}