A hyperparameter optimization framework
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Fast and Accurate ML in 3 Lines of Code
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Automated Machine Learning with scikit-learn
- automl
- scikit-learn
- automated-machine-learning
- hyperparameter-optimization
Distributed Asynchronous Hyperparameter Optimization in Python
- hacktoberfest
Lightning ⚡️ fast forecasting with statistical and econometric models.
- time-series
- statistics
- forecasting
- arima
Scalable and user friendly neural :brain: forecasting algorithms.
- deep-learning
- forecasting
- esrnn
- nbeats
AI Infra / AI Orchestration / AI Control Plane
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
- deep-learning
- machine-learning
- ml-platform
- ml-infrastructure
Automatic architecture search and hyperparameter optimization for PyTorch
- automl
- deep-learning
- tabular-data
- pytorch
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
- hyperopt
- keras
- hyperparameter-optimization
Machine Learning in R
- machine-learning
- data-science
- tuning
- cran
Hyper-parameter optimization for sklearn
Hyperparameter Experiments with TensorFlow and Keras
- deep-learning
- keras
- keras-tensorflow
- hyperparameter-optimization
MLBox is a powerful Automated Machine Learning python library.
- machine-learning
- auto-ml
- kaggle
- deep-learning
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
- bayesian-optimization
- bayesian-optimisation
- hyperparameter-optimization
- hyperparameter-tuning
An auto-evolution framework that optimizes anything — your 7×24 team of algorithm engineers.
- evolutionary-algorithms
- rsi-ai
- self-improvement
- llm-agent
An autoML framework & toolkit for machine learning on graphs.
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
- pipeline
- pipeline-framework
- machine-learning
- deep-learning
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
optimization routines for hyperparameter tuning
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
A hyperparameter optimization framework, inspired by Optuna.
- bayesian-optimization
- bandit-algorithms
- evolution-strategies
- blackbox-optimization