{ "id": 321966, "title": "Don't forget the pets - (Pawpularity's competition)", "url": "/competitions/be-honest-what-do-you-think-of-this-image/discussion/321966", "author_name": "", "post_date": "2022-04-29T14:34:06.613820300Z", "votes": 2, "comment_count": 2, "views": 0, "content": "

Hello Kagglers,

\n

I am opening this discussion to start a thread about sharing good learning from the Pawpularity's competition.

\n

The discussion section was full of good informartions and there is a few of them that I handpicked:

\n\n

(they are the most votes discussion on the pawpularity competition)

\n

Don't hesitate to share your good tips from the petfinder competition 😉

", "messages": [ { "id": "1771748", "postDate": "04/29/2022 14:34:06", "content": "

Hello Kagglers,

\n

I am opening this discussion to start a thread about sharing good learning from the Pawpularity's competition.

\n

The discussion section was full of good informartions and there is a few of them that I handpicked:

\n\n

(they are the most votes discussion on the pawpularity competition)

\n

Don't hesitate to share your good tips from the petfinder competition 😉

", "rawMarkdown": "Hello Kagglers,\n\nI am opening this discussion to start a thread about sharing good learning from the [Pawpularity's competition](https://www.kaggle.com/competitions/petfinder-pawpularity-score/overview).\n\nThe [discussion section](https://www.kaggle.com/competitions/petfinder-pawpularity-score/discussion?sort=votes) was full of good informartions and there is a few of them that I handpicked:\n* [Tricks for Image Classification / Regression with Neural Networks](https://www.kaggle.com/competitions/petfinder-pawpularity-score/discussion/288896)\n* [1st Place - Winning Solution - Full Writeup](https://www.kaggle.com/competitions/petfinder-pawpularity-score/discussion/301686)\n* [CNN Vs Transformer 🔥 Regression Vs Classification](https://www.kaggle.com/competitions/petfinder-pawpularity-score/discussion/275278)\n\n(they are the most votes discussion on the pawpularity competition)\n\nDon't hesitate to share your good tips from the petfinder competition 😉", "votes": null }, { "id": "1773674", "postDate": "05/01/2022 11:18:19", "content": "

Great tips here! I would also add that it's important to use a good data pre-processing pipeline including feature extraction and dimensionality reduction, especially when working with image data. You can also try different architectures and optimization techniques (e.g. dropout, batch normalization, etc.) to improve your models' performance.

", "rawMarkdown": "Great tips here! I would also add that it's important to use a good data pre-processing pipeline including feature extraction and dimensionality reduction, especially when working with image data. You can also try different architectures and optimization techniques (e.g. dropout, batch normalization, etc.) to improve your models' performance.", "votes": null }, { "id": "1781448", "postDate": "05/08/2022 14:16:53", "content": "

Good advices👍

", "rawMarkdown": "Good advices👍", "votes": null } ], "comments": [ { "id": 1773674, "author_name": "", "author_url": "", "post_date": "05/01/2022 11:18:19", "content": "

Great tips here! I would also add that it's important to use a good data pre-processing pipeline including feature extraction and dimensionality reduction, especially when working with image data. You can also try different architectures and optimization techniques (e.g. dropout, batch normalization, etc.) to improve your models' performance.

", "votes": null, "replies": [ { "id": 1781448, "author_name": "jeanmidev", "author_url": "", "post_date": "05/08/2022 14:16:53", "content": "

Good advices👍

", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "1771748": "Hello Kagglers,\n\nI am opening this discussion to start a thread about sharing good learning from the [Pawpularity's competition](https://www.kaggle.com/competitions/petfinder-pawpularity-score/overview).\n\nThe [discussion section](https://www.kaggle.com/competitions/petfinder-pawpularity-score/discussion?sort=votes) was full of good informartions and there is a few of them that I handpicked:\n* [Tricks for Image Classification / Regression with Neural Networks](https://www.kaggle.com/competitions/petfinder-pawpularity-score/discussion/288896)\n* [1st Place - Winning Solution - Full Writeup](https://www.kaggle.com/competitions/petfinder-pawpularity-score/discussion/301686)\n* [CNN Vs Transformer 🔥 Regression Vs Classification](https://www.kaggle.com/competitions/petfinder-pawpularity-score/discussion/275278)\n\n(they are the most votes discussion on the pawpularity competition)\n\nDon't hesitate to share your good tips from the petfinder competition 😉", "1773674": "Great tips here! I would also add that it's important to use a good data pre-processing pipeline including feature extraction and dimensionality reduction, especially when working with image data. You can also try different architectures and optimization techniques (e.g. dropout, batch normalization, etc.) to improve your models' performance.", "1781448": "Good advices👍" }, "source": "meta" }