Meet the Kaggle Grandmasters of NVIDIA, and learn how they use NVIDIA accelerated data science to build winning recommender systems, predict degradation rates in RNA molecules, identify melanoma in medical imaging, and more.
Ahmet Erdem
Senior Data Scientist at NVIDIA
Bo Liu
Chris Deotte
Christof Henkel
Data Scientist at NVIDIA
Gilberto Titericz
Jean-Francois Puget
Distinguished Engineer at NVIDIA
Jiwei Liu
Kazuki Onodera
JUNE 2021
The NVIDIA Merlin and KGMON team earned 1st place in the RecSys Challenge 2021 by effectively predicting the probability of user engagement within a dynamic environment and providing fair recommendations on a multi-million point dataset.
(1:10:47 Minutes)
MARCH 2021
In this recommendation system challenge, the goal was to use a dataset based on millions of real anonymized accommodation reservations to come up with a strategy for making the best recommendation for their next destination, all in real-time.
(49:11 Minutes)
Watch this video to get a short history lesson and the current state of natural language processing and the best practices for using Hugging Face transformers in four different competitions.
(1:06:02 Minutes)
OCTOBER 2020
In this competition, teams were charged with developing machine learning models and designing rules for RNA degradation. The models needed to predict likely degradation rates at each base of an RNA molecule, trained on a subset of an Eterna dataset comprising over 3000 RNA molecules (which span a panoply of sequences and structures) and their degradation rates at each position.
(56:56 Minutes)
SEPTEMBER 2020
In this landmark recognition challenge, the team had to build models that recognize the correct landmark (if any) in a dataset of complicated test images. This is easier said than done, given landmark recognition contains a much larger number of classes. For example, there were more than 81,000 classes in this competition.
AUGUST 2020
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.
The Grandmaster Series is a monthly educational video series for data scientists. In each episode, you'll hear from the world's leading experts in data science as they share their insights, best practices, and key learnings from a recent competition. Tune in and learn how you can apply their learnings to your own data science challenges.
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