Bay Area Women in Machine Learning & Data Science

Our next meetup will be a series of presentations on hyperparameter optimization and how to use various software packages to find a set of optimal model parameters for your machine learning model. Model selection via hyperparameter optimization is an important part of machine learning and we will discuss both the very basic and sophisticated methods for tuning models.  Including: 1.  Cartesian Grid Search2. Random Grid Search3. Tree-structured Parzen Estimators (TPE)4. Bayesian Optimization Schedule: 7:00 – 7:30  Socializing7:30 – 8:00  Erin Craig’s talk8:00 – 8:30  Alexandra’s talk8:30 – 9:00  Erin LeDell’s talk9:00 – 9:30  Wrap up Speaker: Erin CraigTitle: Hyperparameter Optimization: Grid Search and Bayesian OptimizationAbstract: When building a model, how do you select its hyperparameters? Grid search and bayesian optimization are two common methods for hyperparameter optimization; each with its own set…

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