Posts by Scott Sievert (University of Wisconsin–Madison)
Comparing Dask-ML and Ray Tune's Model Selection Algorithms
- Aug 06, 2020
Hyperparameter optimization is the process of deducing model parameters that can’t be learned from data. This process is often time- and resource-consuming, especially in the context of deep learning. A good description of this process can be found at “Tuning the hyper-parameters of an estimator,” and the issues that arise are concisely summarized in Dask-ML’s documentation of “Hyper Parameter Searches.”