An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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Updated
May 10, 2024 - Python
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
A Python implementation of global optimization with gaussian processes.
Automated Machine Learning with scikit-learn
Sequential model-based optimization with a `scipy.optimize` interface
A modular active learning framework for Python
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
a distributed Hyperband implementation on Steroids
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Generalized and Efficient Blackbox Optimization System.
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Bayesian Optimization using GPflow
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
Generalized and Efficient Blackbox Optimization System
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
A fully decentralized hyperparameter optimization framework
Experimental Global Optimization Algorithm
Collection of hyperparameter optimization benchmark problems
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