Bayesian Optimization
Showing 11 of 11 projects
A Python package for constrained global optimization using Bayesian inference and Gaussian processes.
An automated machine learning toolkit that serves as a drop-in replacement for scikit-learn estimators.
A Python library for distributed asynchronous hyperparameter optimization over complex search spaces.
A complete AI-driven process using GANs with LSTM and CNN to predict stock price movements, incorporating diverse data sources and hyperparameter optimization.
A modular library for Bayesian optimization built on PyTorch, enabling efficient optimization of expensive black-box functions.
An easy-to-use, scalable hyperparameter optimization framework for Keras models with define-by-run syntax and built-in search algorithms.
An accessible, general-purpose platform for understanding, managing, deploying, and automating adaptive experiments using Bayesian and bandit optimization.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
Hyperopt-sklearn automates hyperparameter optimization and model selection for scikit-learn machine learning pipelines.
A Bayesian optimization software package for automatically running experiments to minimize an objective in as few runs as possible.
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