#Bayesian-optimization
Showing 24 of 24 repositories tagged #bayesian-optimization, ranked by stars
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
Notebooks about Bayesian methods for machine learning
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
A unified interface for optimization algorithms and experiments
Bayesian Optimization and Design of Experiments
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Parallel Hyperparameter Tuning in Python
An evaluation framework for machine learning models simulating high-throughput materials discovery.
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
Simple, but essential Bayesian optimization package
Python library for Bayesian hyper-parameters optimization
Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive design algorithm, as well as extensions to liquid- and solid-based color matching demos.
TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Python Scripts and Jupyter Notebooks
a PyTorch based Reservoir Computing package with Automatic Hyper-Parameter Tuning
This project leverages spotify's api and provided user playlists to create and tune a neural network model that generates song recommendations based off of song data in provided playlists.