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Weather forecasting with AI, Kaggle tips and tricks, dealing with missing data, deep learning with Jesper Dramsch, The Data Scientist Show #040
Jesper Dramsch is a scientist for machine learning at the European Centre for Medium-Range Weather forecasts. They have a phd in applied Machine Learning to Geoscience from Technical University of Denmark. They are a Kaggle Kernals Expert and TPU star, ranking at top 81/100k worldwide. We talked about weather forecasting, things they learned from Kaggle, how to deal with missing data and ourliers, deep learning, Keras vs Pytorch, XGBoost, their struggles as a phd student, working in the EU vs US. Follow @DalianaLiu for more updates on data science and this show.
(00:01:27) how he got into in ML
(00:09:10) how he handled missing data
(00:28:34) Transformers are eating the world
(00:49:36) Hoover Loss is a fantastic metric to deal with extreme values
(00:54:48) his experience with Kaggle competition
(01:02:59) Kaggle tricks that helped his models perform better
(01:08:18) PyTorch vs Keras
(01:30:30) working in different countries and cultures
Resources shared by Jesper:
The newsletter with missing data:
https://buttondown.email/jesper/archive/towels-have-quite-a-dry-sense-of-humor/
The paper by Gael about missing data:
https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac013/6568998
The Huber Loss:
https://en.wikipedia.org/wiki/Huber_loss
Skill Scores:
https://en.wikipedia.org/wiki/Forecast_skill
Brier Skill in Weather:
https://www.dwd.de/EN/ourservices/seasonals_forecasts/forecast_reliability.html
CRPS Continuous Ranked Probability Score
https://datascience.stackexchange.com/questions/63919/what-is-continuous-ranked-probability-score-crps
ConvNext, Convnets for the 2020s:
https://arxiv.org/abs/2201.03545
Transformers for ensemble forecasts:
https://arxiv.org/abs/2106.13924
Books I recommend:
https://www.amazon.com/shop/jesperdramsch/list/2DYS5KVR5TX0E
Blog posts I wrote about these books:
https://dramsch.net/tags/books/
Short I made about Test-Time Augmentation
https://www.youtube.com/shorts/w4sAh9lKyls
Their links: https://dramsch.net/links
Their open PhD thesis: https://dramsch.net/phd
Newsletter: https://dramsch.net/newsletter
Twitter: https://dramsch.net/twitter
Youtube: https://dramsch.net/youtube
Linkedin: https://dramsch.net/linkedin
Kaggle: https://dramsch.net/
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By Daliana Liu
4.7
7575 ratings
Jesper Dramsch is a scientist for machine learning at the European Centre for Medium-Range Weather forecasts. They have a phd in applied Machine Learning to Geoscience from Technical University of Denmark. They are a Kaggle Kernals Expert and TPU star, ranking at top 81/100k worldwide. We talked about weather forecasting, things they learned from Kaggle, how to deal with missing data and ourliers, deep learning, Keras vs Pytorch, XGBoost, their struggles as a phd student, working in the EU vs US. Follow @DalianaLiu for more updates on data science and this show.
(00:01:27) how he got into in ML
(00:09:10) how he handled missing data
(00:28:34) Transformers are eating the world
(00:49:36) Hoover Loss is a fantastic metric to deal with extreme values
(00:54:48) his experience with Kaggle competition
(01:02:59) Kaggle tricks that helped his models perform better
(01:08:18) PyTorch vs Keras
(01:30:30) working in different countries and cultures
Resources shared by Jesper:
The newsletter with missing data:
https://buttondown.email/jesper/archive/towels-have-quite-a-dry-sense-of-humor/
The paper by Gael about missing data:
https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giac013/6568998
The Huber Loss:
https://en.wikipedia.org/wiki/Huber_loss
Skill Scores:
https://en.wikipedia.org/wiki/Forecast_skill
Brier Skill in Weather:
https://www.dwd.de/EN/ourservices/seasonals_forecasts/forecast_reliability.html
CRPS Continuous Ranked Probability Score
https://datascience.stackexchange.com/questions/63919/what-is-continuous-ranked-probability-score-crps
ConvNext, Convnets for the 2020s:
https://arxiv.org/abs/2201.03545
Transformers for ensemble forecasts:
https://arxiv.org/abs/2106.13924
Books I recommend:
https://www.amazon.com/shop/jesperdramsch/list/2DYS5KVR5TX0E
Blog posts I wrote about these books:
https://dramsch.net/tags/books/
Short I made about Test-Time Augmentation
https://www.youtube.com/shorts/w4sAh9lKyls
Their links: https://dramsch.net/links
Their open PhD thesis: https://dramsch.net/phd
Newsletter: https://dramsch.net/newsletter
Twitter: https://dramsch.net/twitter
Youtube: https://dramsch.net/youtube
Linkedin: https://dramsch.net/linkedin
Kaggle: https://dramsch.net/
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