bayeso
Optimization framework
A framework for optimizing hyperparameters in machine learning models using Bayesian optimization
Simple, but essential Bayesian optimization package
93 stars
5 watching
9 forks
Language: Python
last commit: 11 months ago
Linked from 1 awesome list
bayesian-optimizationhyperparameter-optimizationmachine-learning
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | A Bayesian optimization framework designed to optimize complex functions with robustness and flexibility | 484 |
| | A Python library for automatic optimization of functions through Bayesian optimization | 1,550 |
| | A lightweight Bayesian optimization library designed to optimize expensive-to-evaluate functions using Gaussian Process models and various acquisition functions. | 86 |
| | An optimization framework for machine learning hyperparameters | 1,093 |
| | A Python package for Bayesian optimization using the GPFlow library and TensorFlow. | 270 |
| | A decentralized hyperparameter optimization framework inspired by Optuna. | 262 |
| | A library for solving large-scale optimization problems with flexible and scalable vector and operator definitions | 55 |
| | A framework for distributed optimization with communication compression and optimal oracle complexity. | 0 |
| | A Python-based optimization framework providing tools and algorithms to evolve solutions from problem definitions. | 314 |
| | A framework for fitting time series models with changing parameters and selecting the best model using Bayesian inference | 156 |
| | An approach to train and optimize machine learning models in a decentralized setting by convexifying the optimization process | 4 |
| | A software framework for training neural networks to optimize dielectric metasurfaces using physics-driven generative models and global optimization algorithms. | 101 |
| | A flexible framework for optimizing model parameters in computational neuroscience and related fields. | 204 |
| | A toolset for optimizing hyperparameters of machine learning models using Bayesian optimization and real-time visualization. | 136 |
| | A reinforcement learning-based framework for optimizing hyperparameters in distributed machine learning environments. | 15 |