| tobegit3hub/advisor |
1,202 |
|
0 |
0 |
over 6 years ago |
5 |
October 04, 2018 |
19 |
apache-2.0 |
Jupyter Notebook |
| Open-source implementation of Google Vizier for hyper parameters tuning |
| jaanauati/hyper-search |
135 |
|
2 |
3 |
almost 6 years ago |
19 |
September 07, 2019 |
16 |
|
JavaScript |
| Search-text plugin for Hyper.js |
| jakob-r/mlrHyperopt |
31 |
|
0 |
0 |
over 4 years ago |
0 |
|
10 |
bsd-3-clause |
HTML |
| Easy Hyper Parameter Optimization with mlr and mlrMBO. |
| countif/enas_nni |
25 |
|
0 |
0 |
over 7 years ago |
0 |
|
5 |
|
Python |
| This code is for running enas on nni. |
| jcrvz/customhys |
17 |
|
0 |
0 |
over 2 years ago |
0 |
|
3 |
mit |
Python |
| Customising optimisation metaheuristics via hyper-heuristic search (CUSTOMHyS). This framework provides tools for solving, but not limited to, continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators serve as building blocks for tailoring metaheuristics. They were extracted from ten well-known metaheuristics in the literature. |
| Yard1/hpbandster-sklearn |
15 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
mit |
Python |
| A scikit-learn wrapper for HpBandSter hyper parameter search. |
| Tony607/Keras_BayesianOptimization |
10 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
other |
Jupyter Notebook |
| How to do Hyper-parameters search with Bayesian optimization for Keras model | DLology |
| moro/search_do |
9 |
|
0 |
0 |
almost 18 years ago |
0 |
|
0 |
mit |
Ruby |
| listenaddress/hypergoogle |
9 |
|
0 |
0 |
about 6 years ago |
0 |
|
4 |
|
JavaScript |
| Search Google from the terminal |
| e-dorigatti/hyperband-snakemake |
7 |
|
0 |
0 |
about 4 years ago |
2 |
August 21, 2020 |
0 |
gpl-3.0 |
Python |
| Orchestrate hyper-parameters search with snakemake |
