Posts tagged machine-learning
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.”
Better and faster hyperparameter optimization with Dask
- Sep 30, 2019
Scott Sievert wrote this post. The original post lives at https://stsievert.com/blog/2019/09/27/dask-hyperparam-opt/ with better styling. This work is supported by Anaconda, Inc.