142 Open source projects that are alternatives of or similar to randopt

EGAP methods guides

MIT DeepTraffic top 2% solution (75.01 mph) 🚗.

Library for Semi-Automated Data Science

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

No description or website provided.

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.

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

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

Various experiments using the pixel library

Sequential Model-based Algorithm Configuration

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

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

A general-purpose pythonic genetic algorithm.

A hyperparameter optimization framework

Toolbox for Bayesian Optimization and Model-Based Optimization in R

Experimental Global Optimization Algorithm

Python library for CMA Evolution Strategy.

Hyperparameter optimization for PyTorch.

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

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

Real-time Web Dashboard for Optuna.

A curated list of automated machine learning papers, articles, tutorials, slides and projects

A simple, extensible library for developing AutoML systems

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

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

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

mlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...

Automated modeling and machine learning framework FEDOT

Distribution transparent Machine Learning experiments on Apache Spark

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 with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define m…

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

Yes, it's a camel case.

DiscordLookup | Get more out of Discord with Discord Lookup! Snowflake Decoder, Guild List with Stats, Invite Info and more...

Hyperparameter optimization in Julia.

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

Black box hyperparameter optimization made easy.

Database for experimental neuroscience laboratories

🚀 A demonstration of hyperparameter optimization using Optuna for models implemented with AllenNLP.

[UNMAINTAINED] Automated machine learning for analytics & production

MiraiML: asynchronous, autonomous and continuous Machine Learning in Python

A Clojure machine learning library

🦅Hyperparameter optimization for machine learning pipelines 🦅

A toolset for black-box hyperparameter optimisation.

A shell-friendly hyperparameter search tool inspired by Optuna

Set model hyperparameters using deterministic, exact algorithms.

Machine Learning Tool Box

A fully decentralized hyperparameter optimization framework

A friendly python package for Keras Hyperparameters Tuning based only on NumPy and hyperopt.

A library for composing end-to-end tunable machine learning pipelines.

PyTorch implementation of Proximal Gradient Algorithms a la Parikh and Boyd (2014). Useful for Auto-Sizing (Murray and Chiang 2015, Murray et al. 2019).

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

Random cool stuff I play around using Xamarin.. :3 Some of these cool projects I feature them on my blog, with step by step explanation. :) Don't forget to check it out. Go to: theconfuzedsourcecode.wordpress.com

Scalable, structured, dynamically-scheduled hyperparameter optimization.

Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates

Using Kafka-Python to illustrate a ML production pipeline

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.

Hyperparameter optimization package of the mlr3 ecosystem

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.

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.