Hyperparameter Optimization
Showing 31 of 31 projects
A hyperparameter optimization framework for machine learning with a define-by-run API for dynamic search spaces.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
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.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
An open-source machine learning platform for distributed training, hyperparameter tuning, experiment tracking, and resource management.
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.
Automatic neural architecture search and hyperparameter optimization for PyTorch, focusing on tabular data and time series forecasting.
A simple wrapper that combines Keras and Hyperopt for convenient hyperparameter optimization in deep learning models.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
Hyperopt-sklearn automates hyperparameter optimization and model selection for scikit-learn machine learning pipelines.
A Python library for automated hyperparameter optimization and model evaluation with TensorFlow, Keras, and PyTorch.
Implementation of hyperparameter optimization methods for ML/DL models with sample code for regression and classification tasks.
A web-based tool for automated hyperparameter tuning and stacked ensemble creation in Python.
A versatile Bayesian optimization package for hyperparameter optimization of machine learning algorithms.
An autoML framework and toolkit for automating machine learning tasks on graph-structured data.
A Bitcoin trading bot using deep reinforcement learning (TensorForce) to automate buy/sell/hold decisions based on price history.
A scikit-learn compatible hyperparameter optimization tool using evolutionary algorithms instead of grid search.
An AutoML implementation and tutorial for automating machine learning pipelines on both static datasets and dynamic data streams, with a focus on IoT anomaly detection.
A Python library for interpretable text classification using the SS3 model, with built-in visualization tools for explainable AI.
An R package for automatic optimal predictor ensembling via cross-validation with dozens of machine learning algorithms.
A decentralized hyperparameter optimization framework for Go, inspired by Optuna, supporting Bayesian optimization and evolution strategies.
A Python library for Bayesian optimization using GPflow and TensorFlow, designed for optimizing expensive black-box functions.
An idiomatic Clojure machine learning library providing a unified interface for classification, regression, and unsupervised models.
A tutorial and demo using Hyperopt to auto-optimize CNN architecture and hyperparameters for the CIFAR-100 dataset with Keras/TensorFlow.
A simple yet essential Python framework for Bayesian optimization, enabling efficient hyperparameter tuning and black-box function optimization.
A Python library providing SigOpt hyperparameter optimization wrappers for scikit-learn and XGBoost models.
Provides SigOpt wrappers for scikit-learn to optimize hyperparameters and facilitate model selection.
A fast and versatile implementation of support vector machines with integrated hyper-parameter selection and support for multiple learning scenarios.
Related Tags
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.