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For this session we will be running a hands-on workshop. Expect a brief introduction to the problem, followed by hacking time. Come prepared to code!
For this session we will focus on hyperparameter tuning.
- Skill level: Intermediate
- Prerequisites: Basic proficiency in model development and evaluation
- What to bring: Laptop and ample power. CPU only is fine
- Libraries to install: scikit-learn, scikit-optimize, hyperopt, bayesian-optimization
What we'll work on:
- Implement grid + random search optimization
- Optimize model using at least one Bayesian method
- Compare & contrast tree of parzen estimators, Gaussian processes, regression trees
- Experiment with popular libraries
Space is limited in the venue, so please be mindful of your RSVPs.
A good briefing can be found here https://towardsdatascience.com/a-conceptual-explanation-of-bayesian-model-based-hyperparameter-optimization-for-machine-learning-b8172278050f