{
  "id": 321265,
  "title": "PogChamps #2 Has Ended!",
  "url": "/competitions/kaggle-pog-series-s01e02/discussion/321265",
  "author_name": "",
  "post_date": "2022-04-26T00:18:41.053799200Z",
  "votes": 12,
  "comment_count": 9,
  "views": 0,
  "content": "<p>Thanks everyone who participated in the competition. It was awesome seeing the battle on the leaderboard. I'm really excited to hopefully see some solution write ups.</p>\n<p>If you are in the top 3 and would like to confirm you are eligible for the prize please make your submission notebook public or privately share it with me in the next 48 hours.</p>\n<p>Thanks again so much everyone! I hope it was a great learning experience for everyone involved.</p>",
  "messages": [
    {
      "id": "1768076",
      "postDate": "04/26/2022 00:18:41",
      "content": "<p>Thanks everyone who participated in the competition. It was awesome seeing the battle on the leaderboard. I'm really excited to hopefully see some solution write ups.</p>\n<p>If you are in the top 3 and would like to confirm you are eligible for the prize please make your submission notebook public or privately share it with me in the next 48 hours.</p>\n<p>Thanks again so much everyone! I hope it was a great learning experience for everyone involved.</p>",
      "rawMarkdown": "Thanks everyone who participated in the competition. It was awesome seeing the battle on the leaderboard. I'm really excited to hopefully see some solution write ups.\n\nIf you are in the top 3 and would like to confirm you are eligible for the prize please make your submission notebook public or privately share it with me in the next 48 hours.\n\nThanks again so much everyone! I hope it was a great learning experience for everyone involved.",
      "votes": null
    },
    {
      "id": "1768087",
      "postDate": "04/26/2022 00:55:12",
      "content": "<p>Congratulations to all the top finishers! </p>\n<p>I wanted to participate in this competition but didn't get a chance since I was travelling-I think we all should also sincerely thank Rob for an incredible competition. Throughout the competition, I saw Rob encouraging everyone everywhere-he was really rooting for all of us-be it in new kernels, on the LB, or discussions. Thank you Rob! 🙏</p>\n<p>I'll wait for the final winner announcements and will reach out to trouble people for a chai 😄</p>",
      "rawMarkdown": "Congratulations to all the top finishers! \n\nI wanted to participate in this competition but didn't get a chance since I was travelling-I think we all should also sincerely thank Rob for an incredible competition. Throughout the competition, I saw Rob encouraging everyone everywhere-he was really rooting for all of us-be it in new kernels, on the LB, or discussions. Thank you Rob! 🙏\n\nI'll wait for the final winner announcements and will reach out to trouble people for a chai 😄",
      "votes": null
    },
    {
      "id": "1768146",
      "postDate": "04/26/2022 02:57:16",
      "content": "<p>Thanks a lot, <a href=\"https://www.kaggle.com/robikscube\" target=\"_blank\">@robikscube</a> for this opportunity! I've learned a lot from the competition and from many people here. It's the 1st time I commit that much to a Kaggle competition so, I'm very excited! The inference notebook we can check out here <a href=\"https://www.kaggle.com/code/dienhoa/inference-submission-music-genre\" target=\"_blank\">https://www.kaggle.com/code/dienhoa/inference-submission-music-genre</a> . I will try to upload the training notebook ( I trained locally ) and write solution write-ups.</p>\n<p>Again, thanks a lot </p>",
      "rawMarkdown": "Thanks a lot, @robikscube for this opportunity! I've learned a lot from the competition and from many people here. It's the 1st time I commit that much to a Kaggle competition so, I'm very excited! The inference notebook we can check out here https://www.kaggle.com/code/dienhoa/inference-submission-music-genre . I will try to upload the training notebook ( I trained locally ) and write solution write-ups.\n\nAgain, thanks a lot",
      "votes": null
    },
    {
      "id": "1768166",
      "postDate": "04/26/2022 03:08:53",
      "content": "<p>Congrats on the first place! </p>\n<p>Btw-The notebook is private, could you please change that? :)</p>\n<p>Looking forward to your writeup!</p>",
      "rawMarkdown": "Congrats on the first place! \n\nBtw-The notebook is private, could you please change that? :)\n\nLooking forward to your writeup!",
      "votes": null
    },
    {
      "id": "1768169",
      "postDate": "04/26/2022 03:16:22",
      "content": "<p>Oops. Thanks <a href=\"https://www.kaggle.com/init27\" target=\"_blank\">@init27</a> . Pls find here the notebook <a href=\"https://www.kaggle.com/code/dienhoa/inference-submission-music-genre\" target=\"_blank\">https://www.kaggle.com/code/dienhoa/inference-submission-music-genre</a></p>",
      "rawMarkdown": "Oops. Thanks @init27 . Pls find here the notebook https://www.kaggle.com/code/dienhoa/inference-submission-music-genre",
      "votes": null
    },
    {
      "id": "1768194",
      "postDate": "04/26/2022 04:06:29",
      "content": "<p>Thanks a lot to <a href=\"https://www.kaggle.com/robikscube\" target=\"_blank\">@robikscube</a> for hosting this awesome competition. I wanted to tune my signal processing skills and this competition was a nice way to do so. </p>\n<p>The solution notebook is open at <a href=\"https://www.kaggle.com/code/pheadrus/inference-pipeline-v0/notebook?scriptVersionId=93716687\" target=\"_blank\">Inference</a></p>\n<p>I trained a bunch of models on various spec datasets created using hop length (512, 448), n mel (64, 128, 160) datasets.  I also created CQT transformed datsaets and models on those. I can provide those separately, if needed. </p>\n<p>Thanks,</p>",
      "rawMarkdown": "Thanks a lot to @robikscube for hosting this awesome competition. I wanted to tune my signal processing skills and this competition was a nice way to do so. \n\nThe solution notebook is open at [Inference](https://www.kaggle.com/code/pheadrus/inference-pipeline-v0/notebook?scriptVersionId=93716687)\n\nI trained a bunch of models on various spec datasets created using hop length (512, 448), n mel (64, 128, 160) datasets.  I also created CQT transformed datsaets and models on those. I can provide those separately, if needed. \n\nThanks,",
      "votes": null
    },
    {
      "id": "1768208",
      "postDate": "04/26/2022 04:22:39",
      "content": "<p>Congrats on the 3rd place! :) </p>\n<p>I will request you to have chai soon too 🙏</p>",
      "rawMarkdown": "Congrats on the 3rd place! :) \n\nI will request you to have chai soon too 🙏",
      "votes": null
    },
    {
      "id": "1768217",
      "postDate": "04/26/2022 04:30:54",
      "content": "<p>Looking forward! </p>",
      "rawMarkdown": "Looking forward!",
      "votes": null
    },
    {
      "id": "1768252",
      "postDate": "04/26/2022 05:06:03",
      "content": "<p>You can find here a quick writeup <a href=\"https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/discussion/321281\" target=\"_blank\">https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/discussion/321281</a> , it’s fastai all-in :))</p>",
      "rawMarkdown": "You can find here a quick writeup https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/discussion/321281 , it’s fastai all-in :))",
      "votes": null
    },
    {
      "id": "1768628",
      "postDate": "04/26/2022 13:58:11",
      "content": "<p>Congratulation buddy! 👀😍👌 Fastai</p>",
      "rawMarkdown": "Congratulation buddy! 👀😍👌 Fastai",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1768087,
      "author_name": "init27",
      "author_url": "",
      "post_date": "04/26/2022 00:55:12",
      "content": "<p>Congratulations to all the top finishers! </p>\n<p>I wanted to participate in this competition but didn't get a chance since I was travelling-I think we all should also sincerely thank Rob for an incredible competition. Throughout the competition, I saw Rob encouraging everyone everywhere-he was really rooting for all of us-be it in new kernels, on the LB, or discussions. Thank you Rob! 🙏</p>\n<p>I'll wait for the final winner announcements and will reach out to trouble people for a chai 😄</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1768146,
      "author_name": "dienhoa",
      "author_url": "",
      "post_date": "04/26/2022 02:57:16",
      "content": "<p>Thanks a lot, <a href=\"https://www.kaggle.com/robikscube\" target=\"_blank\">@robikscube</a> for this opportunity! I've learned a lot from the competition and from many people here. It's the 1st time I commit that much to a Kaggle competition so, I'm very excited! The inference notebook we can check out here <a href=\"https://www.kaggle.com/code/dienhoa/inference-submission-music-genre\" target=\"_blank\">https://www.kaggle.com/code/dienhoa/inference-submission-music-genre</a> . I will try to upload the training notebook ( I trained locally ) and write solution write-ups.</p>\n<p>Again, thanks a lot </p>",
      "votes": null,
      "replies": [
        {
          "id": 1768166,
          "author_name": "init27",
          "author_url": "",
          "post_date": "04/26/2022 03:08:53",
          "content": "<p>Congrats on the first place! </p>\n<p>Btw-The notebook is private, could you please change that? :)</p>\n<p>Looking forward to your writeup!</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 1768169,
          "author_name": "dienhoa",
          "author_url": "",
          "post_date": "04/26/2022 03:16:22",
          "content": "<p>Oops. Thanks <a href=\"https://www.kaggle.com/init27\" target=\"_blank\">@init27</a> . Pls find here the notebook <a href=\"https://www.kaggle.com/code/dienhoa/inference-submission-music-genre\" target=\"_blank\">https://www.kaggle.com/code/dienhoa/inference-submission-music-genre</a></p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 1768252,
          "author_name": "dienhoa",
          "author_url": "",
          "post_date": "04/26/2022 05:06:03",
          "content": "<p>You can find here a quick writeup <a href=\"https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/discussion/321281\" target=\"_blank\">https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/discussion/321281</a> , it’s fastai all-in :))</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 1768628,
          "author_name": "venkatkumar001",
          "author_url": "",
          "post_date": "04/26/2022 13:58:11",
          "content": "<p>Congratulation buddy! 👀😍👌 Fastai</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 1768194,
      "author_name": "pheadrus",
      "author_url": "",
      "post_date": "04/26/2022 04:06:29",
      "content": "<p>Thanks a lot to <a href=\"https://www.kaggle.com/robikscube\" target=\"_blank\">@robikscube</a> for hosting this awesome competition. I wanted to tune my signal processing skills and this competition was a nice way to do so. </p>\n<p>The solution notebook is open at <a href=\"https://www.kaggle.com/code/pheadrus/inference-pipeline-v0/notebook?scriptVersionId=93716687\" target=\"_blank\">Inference</a></p>\n<p>I trained a bunch of models on various spec datasets created using hop length (512, 448), n mel (64, 128, 160) datasets.  I also created CQT transformed datsaets and models on those. I can provide those separately, if needed. </p>\n<p>Thanks,</p>",
      "votes": null,
      "replies": [
        {
          "id": 1768208,
          "author_name": "init27",
          "author_url": "",
          "post_date": "04/26/2022 04:22:39",
          "content": "<p>Congrats on the 3rd place! :) </p>\n<p>I will request you to have chai soon too 🙏</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 1768217,
          "author_name": "pheadrus",
          "author_url": "",
          "post_date": "04/26/2022 04:30:54",
          "content": "<p>Looking forward! </p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "1768076": "Thanks everyone who participated in the competition. It was awesome seeing the battle on the leaderboard. I'm really excited to hopefully see some solution write ups.\n\nIf you are in the top 3 and would like to confirm you are eligible for the prize please make your submission notebook public or privately share it with me in the next 48 hours.\n\nThanks again so much everyone! I hope it was a great learning experience for everyone involved.",
    "1768087": "Congratulations to all the top finishers! \n\nI wanted to participate in this competition but didn't get a chance since I was travelling-I think we all should also sincerely thank Rob for an incredible competition. Throughout the competition, I saw Rob encouraging everyone everywhere-he was really rooting for all of us-be it in new kernels, on the LB, or discussions. Thank you Rob! 🙏\n\nI'll wait for the final winner announcements and will reach out to trouble people for a chai 😄",
    "1768146": "Thanks a lot, @robikscube for this opportunity! I've learned a lot from the competition and from many people here. It's the 1st time I commit that much to a Kaggle competition so, I'm very excited! The inference notebook we can check out here https://www.kaggle.com/code/dienhoa/inference-submission-music-genre . I will try to upload the training notebook ( I trained locally ) and write solution write-ups.\n\nAgain, thanks a lot",
    "1768166": "Congrats on the first place! \n\nBtw-The notebook is private, could you please change that? :)\n\nLooking forward to your writeup!",
    "1768169": "Oops. Thanks @init27 . Pls find here the notebook https://www.kaggle.com/code/dienhoa/inference-submission-music-genre",
    "1768194": "Thanks a lot to @robikscube for hosting this awesome competition. I wanted to tune my signal processing skills and this competition was a nice way to do so. \n\nThe solution notebook is open at [Inference](https://www.kaggle.com/code/pheadrus/inference-pipeline-v0/notebook?scriptVersionId=93716687)\n\nI trained a bunch of models on various spec datasets created using hop length (512, 448), n mel (64, 128, 160) datasets.  I also created CQT transformed datsaets and models on those. I can provide those separately, if needed. \n\nThanks,",
    "1768208": "Congrats on the 3rd place! :) \n\nI will request you to have chai soon too 🙏",
    "1768217": "Looking forward!",
    "1768252": "You can find here a quick writeup https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/discussion/321281 , it’s fastai all-in :))",
    "1768628": "Congratulation buddy! 👀😍👌 Fastai"
  },
  "source": "meta"
}