machinelearningnuremberg/DPL
[NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
This project helps machine learning engineers efficiently find the best settings (hyperparameters) for their models. You provide your machine learning model and dataset, and it automatically explores different configurations, intelligently pausing less promising ones and focusing on those that show the most potential. This results in highly optimized model performance with less trial and error.
No commits in the last 6 months.
Use this if you are a machine learning practitioner looking to automatically tune your model's hyperparameters to achieve state-of-the-art performance across various datasets without extensive manual effort.
Not ideal if you are looking for a simple, out-of-the-box solution without any Python environment setup or configuration, or if your models do not produce measurable learning curves.
Stars
16
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 12, 2023
Commits (30d)
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