There are 41 repositories under hyperparameter-optimization topic.
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A hyperparameter optimization framework
Automated Machine Learning with scikit-learn
AutoGluon: AutoML for Image, Text, and Tabular Data
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
A curated list of automated machine learning papers, articles, tutorials, slides and projects
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Sequential model-based optimization with a `scipy.optimize` interface
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A fast library for AutoML and tuning.
Determined: Deep Learning Training Platform
[UNMAINTAINED] Automated machine learning for analytics & production
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.
Python library to easily log experiments and parallelize hyperparameter search for neural networks
Sequential Model-based Algorithm Configuration
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Tuning hyperparams fast with Hyperband
a distributed Hyperband implementation on Steroids
Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).
The world's cleanest AutoML framework ✨ - 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 frameworks and cloud environments.
Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.
Experimental Global Optimization Algorithm
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
Hyperparameter optimization for PyTorch.
Automated modeling and machine learning framework FEDOT
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)