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/