#hyperparameter-optimization (18 Repositories)
Ranked open-source repositories tagged with #hyperparameter-optimization, scored by pull request acceptance likelihood and maintainer engagement velocity.
30.1%
19.1h
18 repositories tagged #hyperparameter-optimization
aimclub/FEDOT
Automated modeling and machine learning framework FEDOT
rodrigo-arenas/Sklearn-genetic-opt
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
optuna/optuna
A hyperparameter optimization framework
DLR-RM/rl-baselines3-zoo
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
syne-tune/syne-tune
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
IBM/lale
Library for Semi-Automated Data Science
CyberAgentAILab/cmaes
Python library for CMA Evolution Strategy.
Optim-Agent/optim-agent
LLM agents as your hyperparameter optimizer.
kruize/autotune
Autonomous Performance Tuning for Kubernetes!
datarobot/syftr
syftr is an agent optimizer that helps you find the best agentic workflows for your budget.
chrisstroemel/Simple
Experimental Global Optimization Algorithm
sherpa-ai/sherpa
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
leopard-ai/betty
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
TreborNamor/TradingView-Machine-Learning-GUI
HyperView is a terminal-first TradingView strategy lab for downloading market data, backtesting Python strategies with Pine-like behavior, and optimizing SL/TP parameters.
keras-team/keras-tuner
A Hyperparameter Tuning Library for Keras
baggepinnen/Hyperopt.jl
Hyperparameter optimization in Julia.
autonomio/talos
Hyperparameter Experiments with TensorFlow and Keras
determined-ai/determined
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.