Explore efficiently automated parallel hyperparameter search for optimizing machine learning models over large scale cloud cluster

Machine learning has been applied in various fields and shown promising results in recent years. Researchers have found that tuning machine learning models in a proper way can vastly boost the model performance with respect to the specific AI task. However, tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming. There is therefore great appeal for automatic approaches that can optimize the hyperparameter of any given model. This project aims to provide an end to end automotive hyperparameter search framework that can help people explore better machine learning models

Intern: 
Jiahuang Lin
Faculty Supervisor: 
Gennady Pekhimenko
Province: 
Ontario
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