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The Top 54 Hyperparameter Optimization Open Source Projects
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.
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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.
Automated Machine Learning with scikit-learn
A hyperparameter optimization framework
AutoGluon: AutoML for Text, Image, and Tabular Data
Awesome Automl Papers
A curated list of automated machine learning papers, articles, tutorials, slides and projects
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
[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.
Determined: Deep Learning Training Platform
Rl Baselines Zoo
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Awesome Automl And Lightweight Models
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
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀
Tuning hyperparams fast with Hyperband
Sequential Model-based Algorithm Configuration
Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).
Experimental Global Optimization Algorithm
a distributed Hyperband implementation on Steroids
Hyperparameter optimization for PyTorch.
Hyperparameter Optimization Of Machine Learning Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
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.
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
Awesome Distributed Deep Learning
A curated list of awesome Distributed Deep Learning resources.
Python code for bayesian optimization using Gaussian processes
Bayesian Optimization using GPflow
My Data Competition Experience
A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
Asynchronous Distributed Hyperparameter Optimization.
Library for Semi-Automated Data Science
Toolbox for Bayesian Optimization and Model-Based Optimization in R
Gradient based hyperparameter optimization & meta-learning package for TensorFlow
A simple, extensible library for developing AutoML systems
An automatic ML model optimization tool.
mlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...
DeepArchitect: Automatically Designing and Training Deep Architectures
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
Time Series Cross-Validation -- an extension for scikit-learn
A toolset for black-box hyperparameter optimisation.
Bilevel Optimization Library in Python for Multi-Task and Meta Learning
A fully decentralized hyperparameter optimization framework
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
A general, modular, and programmable architecture search framework
Hyperopt Keras Cnn Cifar 100
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Rl Baselines3 Zoo
A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.
Purely functional genetic algorithms for multi-objective optimisation
Simple, but essential Bayesian optimization package
MIT DeepTraffic top 2% solution (75.01 mph) 🚗.
Coursera Deep Learning Specialization
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
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