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Profiling Top Kagglers: Bestfitting, Currently #1 in the World

Kaggle Team|

We have a new #1 on our leaderboard – a competitor who surprisingly joined the platform just two years ago. Shubin Dai, better known as Bestfitting on Kaggle or Bingo by his friends, is a data scientist and engineering manager living in Changsha, China. He currently leads a company he founded that provides software solutions to banks. Outside of work, and off Kaggle, Dai’s an avid mountain biker and enjoys spending time in nature. Here’s Bestfitting: Can you tell us ...

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Quarterly product update: Create your data science projects on Kaggle

Ben Hamner|

We’re building Kaggle into a platform where you can collaboratively create all of your data science projects. This past quarter, we’ve increased the breadth and scope of work you can build on our platform by launching many new features and expanding computational resources. It is now possible for you to load private datasets you’re working with, develop complex analyses on them in our cloud-based data science environment, and share the project with collaborators in a reproducible way.

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From Kaggle competition to start-up and tracking 2 million km² of forest

Mark McDonald|

This is a guest post written by Kaggle Competition Master and  part of a team that achieved 5th position in the 'Planet: Understanding the Amazon from Space' competition, Indra den Bakker. In this post, he shares the journey from Kaggle competition winner to start-up founder focused on tracking deforestation and other forest management insights. Back in the days, during my studies I was introduced to Kaggle. For the course ‘Data Mining Techniques’ at VU University Amsterdam we had to compete in the ...

Product Update: Create and Manage Datasets from the Command Line using the Official Kaggle API

Megan Risdal|

Kaggle Datasets API Tutorial

Have you used Kaggle's beta API to download data or make a competition submission? We're pleased to announce version 1.1 of the API which includes new features for easily managing your datasets on Kaggle from the command line. Read on to learn how to use the API to create and update datasets or check out detailed documentation on our GitHub page. Create new datasets » After you follow the installation instructions, it's simple to create a new dataset on Kaggle ...

A Brief Summary of the Kaggle Text Normalization Challenge

Richard Sproat|

This post is written by Richard Sproat & Kyle Gorman from Google's Speech & Language Algorithms Team. They hosted the recent, Text Normalization Challenges. Bios below. Now that the Kaggle Text Normalization Challenges for English and Russian are over, we would once again like to thank the hundreds of teams who participated and submitted results, and congratulate the three teams that won in each challenge. The purpose of this note is to summarize what we felt we learned from this competition ...

Our Final Kaggle Dataset Publishing Awards Winners' Interviews (November 2017 and December 2017)

Megan Risdal|

As we move into 2018, the monthly Datasets Publishing Awards has concluded. We're pleased to have recognized many publishers of high-quality, original, and impactful datasets. It was only a little over a year ago that we opened up our public Datasets platform to data enthusiasts all over the world to share their work. We've now reached almost 10,000 public datasets, making choosing winners each month a difficult task! These interviews feature the stories and backgrounds of the November and December ...

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Reviewing 2017 and Previewing 2018

Anthony Goldbloom|

2017 was a huge year for Kaggle. Aside from joining Google, it also marks the year that our community expanded from being primarily focused on machine learning competitions to a broader data science and machine learning platform. This year our public Datasets platform and Kaggle Kernels both grew ~3x, meaning we now also have a thriving data repository and code sharing environment.  Each of those products are on track to pass competitions on most activity metrics in early 2018. To ...

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An Intuitive Introduction to Generative Adversarial Networks

Keshav Dhandhania|

This article was jointly written by Keshav Dhandhania and Arash Delijani, bios below. In this article, I’ll talk about Generative Adversarial Networks, or GANs for short. GANs are one of the very few machine learning techniques which has given good performance for generative tasks, or more broadly unsupervised learning. In particular, they have given splendid performance for a variety of image generation related tasks. Yann LeCun, one of the forefathers of deep learning, has called them “the best idea in ...