Name
Abstract | ||
|---|---|---|
The apsis toolkit presented in this paper provides a flexible framework for hyperparameter optimization and includes both random search and a bayesian optimizer. It is implemented in Python and its architecture features adaptability to any desired machine learning code. It can easily be used with common Python ML frameworks such as scikit-learn. Published under the MIT License other researchers are heavily encouraged to check out the code, contribute or raise any suggestions. The code can be found at github.com/FrederikDiehl/apsis. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Adaptability,Hyperparameter optimization,Random search,Architecture,Computer science,MIT License,Apsis,Artificial intelligence,Machine learning,Python (programming language),Bayesian probability |
DocType | Volume | Citations |
Journal | abs/1503.02946 | 0 |
PageRank | References | Authors |
0.34 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frederik Diehl | 1 | 8 | 2.24 |
| Andreas Jauch | 2 | 0 | 0.34 |