By Hans Tierens
You all know that datarootsians are excellent data athletes. Olympics athletes train with weights, we evolved past that mere display of physical strength and started training the weights. In this way, Machine Learning Engineers train our models to achieve optimal performance on any task given to us. That’s how we shine!
In this tutorial, we explain how state of the art hyper parameters techniques work and when to apply them, using Optuna library.
The code
GitHub - datarootsio/tutorial-hyperparameter-optimization: Tutorial for Rootlabs@Lunch: Practical Hyperparameter Optimisation
Tutorial for Rootlabs@Lunch: Practical Hyperparameter Optimisation - GitHub - datarootsio/tutorial-hyperparameter-optimization: Tutorial for Rootlabs@Lunch: Practical Hyperparameter Optimisation
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