<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Kaggle on DeepLearning.Earth</title><link>https://deeplearning.earth/tags/kaggle/</link><description>Recent content in Kaggle on DeepLearning.Earth</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Thu, 07 Jul 2022 11:26:11 +0200</lastBuildDate><atom:link href="https://deeplearning.earth/tags/kaggle/index.xml" rel="self" type="application/rss+xml"/><item><title>Important Things you should know before Organizing a Kaggle Competition</title><link>https://deeplearning.earth/posts/2020-03-08_important-things-you-should-know-before-organizing-a-kaggle-competition/</link><pubDate>Sun, 08 Mar 2020 20:45:30 +0000</pubDate><guid>https://deeplearning.earth/posts/2020-03-08_important-things-you-should-know-before-organizing-a-kaggle-competition/</guid><description>Lessons Learned from the Airbus Ship Detection Challenge Link to heading In 2018, my team at Airbus Defence and Space Intelligence and I organized a machine learning challenge on the well-known Kaggle platform (see https://www.kaggle.com/c/airbus-ship-detection). At the time, there were discussions internally about the interest of such an initiative, the concrete benefits that we could gain from it, and the potential risks of open-sourcing our imagery.
One year later, we have no doubt that it was worth it.</description></item></channel></rss>