Grandmaster Series – How to Build a World-Class ML Model for Melanoma Detection

In episode one of the Grandmaster Series you’ll learn from three members of the Kaggle Grandmasters of NVIDIA (KGMON) team Chris Deotte, Bo Liu, and Gilberto Titericz.

Watch the video below to learn how they built the winning ML model for the SIIM-ISIC Melanoma Classification Kaggle competition. 

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In this competition, the team had to create ML models to identify skin lesions from patients’ images and determine which images are most likely to represent a melanoma. The winning ML model was able to identify melanoma earlier and more accurately than the average dermatologist. 

If you have any questions during the video, you can submit them through chat. We will try to provide answers throughout and at the end of the episode.

About our presenters: 

  • Chris Deotte – is a senior data scientist at NVIDIA. Chris has a Ph.D. in computational science and mathematics with a thesis on optimizing parallel processing. Chris is a Kaggle 4x grandmaster.
  • Bo Liu – holds a Ph.D. in Applied Math and Statistics from Johns Hopkins University. Bo spent time working in Fintech and while doing so was competing in his free time and earning his Kaggle Grandmaster’s title. Bo joined NVIDIA in May of 2020 and is mostly interested in deep learning competitions, especially those related to computer vision.
  • Gilberto Titericz – known as Giba, is currently a senior data scientist at NVIDIA. Prior to NVIDIA, he worked at Ople, Airbnb, Petrobras, and Siemens. Gilberto had held the #1 position at Kaggle for more than two years. 

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