Rl Baselines3 ZooA collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.
Stars: ✭ 161 (-86.25%)
syne-tuneLarge scale and asynchronous Hyperparameter Optimization at your fingertip.
Stars: ✭ 105 (-91.03%)
mlr3tuningHyperparameter optimization package of the mlr3 ecosystem
Stars: ✭ 44 (-96.24%)
Rl Baselines ZooA collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Stars: ✭ 839 (-28.35%)
Auto SklearnAutomated Machine Learning with scikit-learn
Stars: ✭ 5,916 (+405.21%)
PbtPopulation Based Training (in PyTorch with sqlite3). Status: Unsupported
Stars: ✭ 138 (-88.22%)
Smac3Sequential Model-based Algorithm Configuration
Stars: ✭ 564 (-51.84%)
LaleLibrary for Semi-Automated Data Science
Stars: ✭ 198 (-83.09%)
Hyperparameter hunterEasy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Stars: ✭ 648 (-44.66%)
Coursera Deep Learning SpecializationNotes, 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
Stars: ✭ 188 (-83.95%)
mindwareAn efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Stars: ✭ 34 (-97.1%)
mltbMachine Learning Tool Box
Stars: ✭ 25 (-97.87%)
bboptBlack box hyperparameter optimization made easy.
Stars: ✭ 66 (-94.36%)
AuptimizerAn automatic ML model optimization tool.
Stars: ✭ 166 (-85.82%)
polystoresA library for performing hyperparameter optimization
Stars: ✭ 48 (-95.9%)
Scikit OptimizeSequential model-based optimization with a `scipy.optimize` interface
Stars: ✭ 2,258 (+92.83%)
scikit-hyperbandA scikit-learn compatible implementation of hyperband
Stars: ✭ 68 (-94.19%)
NeuraxleA 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.
Stars: ✭ 377 (-67.81%)
Hyperopt Keras Cnn Cifar 100Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Stars: ✭ 95 (-91.89%)
Automl alexState-of-the art Automated Machine Learning python library for Tabular Data
Stars: ✭ 132 (-88.73%)
SherpaHyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
Stars: ✭ 289 (-75.32%)
MilanoMilano is a tool for automating hyper-parameters search for your models on a backend of your choice.
Stars: ✭ 140 (-88.04%)
naturalselectionA general-purpose pythonic genetic algorithm.
Stars: ✭ 17 (-98.55%)
mangoParallel Hyperparameter Tuning in Python
Stars: ✭ 241 (-79.42%)
MgoPurely functional genetic algorithms for multi-objective optimisation
Stars: ✭ 63 (-94.62%)
HypernetsA General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
Stars: ✭ 221 (-81.13%)
maggyDistribution transparent Machine Learning experiments on Apache Spark
Stars: ✭ 83 (-92.91%)
HyperbandTuning hyperparams fast with Hyperband
Stars: ✭ 555 (-52.6%)
Meta-SACAuto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020
Stars: ✭ 19 (-98.38%)
tunetaIntelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
Stars: ✭ 77 (-93.42%)
AtmAuto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).
Stars: ✭ 504 (-56.96%)
FEDOTAutomated modeling and machine learning framework FEDOT
Stars: ✭ 312 (-73.36%)
OptunaA hyperparameter optimization framework
Stars: ✭ 5,679 (+384.97%)
kerastuneRR interface to Keras Tuner
Stars: ✭ 28 (-97.61%)
RobynRobyn 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…
Stars: ✭ 433 (-63.02%)
Deep trafficMIT DeepTraffic top 2% solution (75.01 mph) 🚗.
Stars: ✭ 47 (-95.99%)
Gradient Free OptimizersSimple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Stars: ✭ 711 (-39.28%)
Hpbandstera distributed Hyperband implementation on Steroids
Stars: ✭ 456 (-61.06%)
Auto-SurpriseAn AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀
Stars: ✭ 19 (-98.38%)
mlrHyperoptEasy Hyper Parameter Optimization with mlr and mlrMBO.
Stars: ✭ 30 (-97.44%)
SimpleExperimental Global Optimization Algorithm
Stars: ✭ 450 (-61.57%)
hyper-enginePython library for Bayesian hyper-parameters optimization
Stars: ✭ 80 (-93.17%)
Awesome Automl And Lightweight ModelsA 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.
Stars: ✭ 691 (-40.99%)
OnepanelThe open and extensible integrated development environment (IDE) for computer vision with built-in modules for model building, automated labeling, data processing, model training, hyperparameter tuning and workflow orchestration.
Stars: ✭ 428 (-63.45%)
Hyperopt.jlHyperparameter optimization in Julia.
Stars: ✭ 144 (-87.7%)
codeflareSimplifying the definition and execution, scaling and deployment of pipelines on the cloud.
Stars: ✭ 163 (-86.08%)
BayesoSimple, but essential Bayesian optimization package
Stars: ✭ 57 (-95.13%)
MlprimitivesPrimitives for machine learning and data science.
Stars: ✭ 46 (-96.07%)
Test TubePython library to easily log experiments and parallelize hyperparameter search for neural networks
Stars: ✭ 663 (-43.38%)
HypersearchHyperparameter optimization for PyTorch.
Stars: ✭ 376 (-67.89%)
optuna-allennlp🚀 A demonstration of hyperparameter optimization using Optuna for models implemented with AllenNLP.
Stars: ✭ 17 (-98.55%)
HyperboardA web-based dashboard for Deep Learning
Stars: ✭ 336 (-71.31%)
cmaesPython library for CMA Evolution Strategy.
Stars: ✭ 174 (-85.14%)
cerebro-systemData System for Optimized Deep Learning Model Selection
Stars: ✭ 15 (-98.72%)
AutogluonAutoGluon: AutoML for Text, Image, and Tabular Data
Stars: ✭ 3,920 (+234.76%)
miraimlMiraiML: asynchronous, autonomous and continuous Machine Learning in Python
Stars: ✭ 23 (-98.04%)