Jonte DanckerinTowards Data ScienceWhy You (Currently) Do Not Need Deep Learning for Time Series ForecastingWhat you need instead: Learnings from the Makridakis M5 competitions and the 2023 Kaggle AI reportJun 2015Jun 2015
Jonte DanckerinTowards Data ScienceN-HiTS — Making Deep Learning for Time Series Forecasting More EfficientA deep dive into how N-HiTS works and how you can use itMay 301May 301
Jonte DanckerinTowards Data ScienceN-BEATS — The First Interpretable Deep Learning Model That Worked for Time Series ForecastingAn easy-to-understand deep dive into how N-BEATS works and how you can use it.May 113May 113
Jonte DanckerinTowards Data ScienceAll You Need Is Conformal PredictionAn important but easy-to-use tool for uncertainty quantification every data scientist should know.Apr 303Apr 303
Jonte DanckerinTowards Data ScienceUncertainty Quantification and Why You Should CareHow to improve your ML model with three lines of codeApr 246Apr 246
Jonte DanckerinTowards AIA Recipe For a Robust Model Development ProcessSix steps to reach high confidence in your model and development processApr 8Apr 8
Jonte DanckerinTowards AIWhy You Should Care About Business Metrics in Your Next ML ProjectBusiness metrics matter. They can kill your entire ML project.Mar 29Mar 29
Jonte DanckerinTowards AILearning Curves: A Picture Says More Than a Thousand WordsA valuable tool to understand ML modelsMar 191Mar 191
Jonte DanckerinTowards AIWhy You Should Always Start With a Baseline ModelA baseline model takes 10 % of the time to develop but gets us 90 % of the way to achieve reasonable results.Mar 6Mar 6
Jonte DanckerThe Lifecycle of the Test SetThe integrity of the test set is crucial in evaluating model performance. We want the test set to give us an unbiased estimate of how well…Mar 2Mar 2
