{
  "id": 116729,
  "title": "Dae Chan Han [2019-PR-Midterm-ImageClassification]",
  "url": "/competitions/2019-fall-pr-project/discussion/116729",
  "author_name": "Dae Chan Han",
  "post_date": "2019-11-11T04:52:04.386000",
  "votes": 0,
  "comment_count": 0,
  "views": 0,
  "content": "<h2>1</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2F06218a89c11b9e2e93eb220176c88b05%2Fkaggleimg.PNG?generation=1573447182542581&amp;alt=media\" alt=\"\">\n데이터 읽어오기</p>\n\n<h2>2</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Ff39ada711ef08635094e283c1c07d882%2Fkaggleimg2.PNG?generation=1573447184551661&amp;alt=media\" alt=\"\">\ntrain 과 validation 나누기 train이 너무 많아 시간이 오래 걸리므로 2000장만 사용합니ㅏㄷ</p>\n\n<h2>3</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2F0b07d5c1cbf0db91ea954ac562df7172%2Fkaggleimg3.PNG?generation=1573447184396063&amp;alt=media\" alt=\"\">\nRbf kernal trick 을 사용 하고 grid search를 사용해 최적의 파라미터를 계산</p>\n\n<h2>4</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Fe985a493fc72be8c8bed20775684d259%2Fkaggleimg4.PNG?generation=1573447184014376&amp;alt=media\" alt=\"\">\ntest 데이터 읽어오기</p>\n\n<h2>5</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Fcdf2757faf74f9ab429875de7fa5caf5%2Fkaggleimg5.PNG?generation=1573447183962970&amp;alt=media\" alt=\"\">\ncsv 만들기</p>",
  "messages": [
    {
      "id": 670146,
      "postDate": "2019-11-11T04:52:04.387Z",
      "content": "<h2>1</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2F06218a89c11b9e2e93eb220176c88b05%2Fkaggleimg.PNG?generation=1573447182542581&amp;alt=media\" alt=\"\">\n데이터 읽어오기</p>\n\n<h2>2</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Ff39ada711ef08635094e283c1c07d882%2Fkaggleimg2.PNG?generation=1573447184551661&amp;alt=media\" alt=\"\">\ntrain 과 validation 나누기 train이 너무 많아 시간이 오래 걸리므로 2000장만 사용합니ㅏㄷ</p>\n\n<h2>3</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2F0b07d5c1cbf0db91ea954ac562df7172%2Fkaggleimg3.PNG?generation=1573447184396063&amp;alt=media\" alt=\"\">\nRbf kernal trick 을 사용 하고 grid search를 사용해 최적의 파라미터를 계산</p>\n\n<h2>4</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Fe985a493fc72be8c8bed20775684d259%2Fkaggleimg4.PNG?generation=1573447184014376&amp;alt=media\" alt=\"\">\ntest 데이터 읽어오기</p>\n\n<h2>5</h2>\n\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Fcdf2757faf74f9ab429875de7fa5caf5%2Fkaggleimg5.PNG?generation=1573447183962970&amp;alt=media\" alt=\"\">\ncsv 만들기</p>",
      "rawMarkdown": "## 1\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2F06218a89c11b9e2e93eb220176c88b05%2Fkaggleimg.PNG?generation=1573447182542581&amp;alt=media)\n데이터 읽어오기\n## 2\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Ff39ada711ef08635094e283c1c07d882%2Fkaggleimg2.PNG?generation=1573447184551661&amp;alt=media)\ntrain 과 validation 나누기 train이 너무 많아 시간이 오래 걸리므로 2000장만 사용합니ㅏㄷ\n\n## 3\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2F0b07d5c1cbf0db91ea954ac562df7172%2Fkaggleimg3.PNG?generation=1573447184396063&amp;alt=media)\nRbf kernal trick 을 사용 하고 grid search를 사용해 최적의 파라미터를 계산\n## 4\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Fe985a493fc72be8c8bed20775684d259%2Fkaggleimg4.PNG?generation=1573447184014376&amp;alt=media)\ntest 데이터 읽어오기\n## 5\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Fcdf2757faf74f9ab429875de7fa5caf5%2Fkaggleimg5.PNG?generation=1573447183962970&amp;alt=media)\ncsv 만들기"
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "670146": "## 1\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2F06218a89c11b9e2e93eb220176c88b05%2Fkaggleimg.PNG?generation=1573447182542581&amp;alt=media)\n데이터 읽어오기\n## 2\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Ff39ada711ef08635094e283c1c07d882%2Fkaggleimg2.PNG?generation=1573447184551661&amp;alt=media)\ntrain 과 validation 나누기 train이 너무 많아 시간이 오래 걸리므로 2000장만 사용합니ㅏㄷ\n\n## 3\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2F0b07d5c1cbf0db91ea954ac562df7172%2Fkaggleimg3.PNG?generation=1573447184396063&amp;alt=media)\nRbf kernal trick 을 사용 하고 grid search를 사용해 최적의 파라미터를 계산\n## 4\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Fe985a493fc72be8c8bed20775684d259%2Fkaggleimg4.PNG?generation=1573447184014376&amp;alt=media)\ntest 데이터 읽어오기\n## 5\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3879542%2Fcdf2757faf74f9ab429875de7fa5caf5%2Fkaggleimg5.PNG?generation=1573447183962970&amp;alt=media)\ncsv 만들기"
  }
}