540 Open source projects that are alternatives of or similar to mango

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

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Sequential model-based optimization with a `scipy.optimize` interface

Black box hyperparameter optimization made easy.

Distribution transparent Machine Learning experiments on Apache Spark

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

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Python library for Bayesian hyper-parameters optimization

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Generalized and Efficient Blackbox Optimization System.

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Machine Learning Tool Box

Hyperparameter optimization in Julia.

Yes, it's a camel case.

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A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

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Easy Hyper Parameter Optimization with mlr and mlrMBO.

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Purely functional genetic algorithms for multi-objective optimisation

Determined: Deep Learning Training Platform

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

GPstuff - Gaussian process models for Bayesian analysis

Population Based Training (in PyTorch with sqlite3). Status: Unsupported

A library for performing hyperparameter optimization

A fully decentralized hyperparameter optimization framework

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

A library for doing Bayesian Optimization using Gaussian Processes (blackbox optimizer) in Go/Golang.

a distributed Hyperband implementation on Steroids

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

Simple, but essential Bayesian optimization package

Library for Semi-Automated Data Science

A scikit-learn compatible implementation of hyperband

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

State-of-the art Automated Machine Learning python library for Tabular Data

A toolset for black-box hyperparameter optimisation.

A simple, extensible library for developing AutoML systems

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Toolbox for Bayesian Optimization and Model-Based Optimization in R

Bayesian Optimization using GPflow

An automatic ML model optimization tool.

A Python implementation of global optimization with gaussian processes.

This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.

Python code for bayesian optimization using Gaussian processes

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.