hyperparameter-optimization
There are 993 repositories under hyperparameter-optimization topic.
ray-project/ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
d2l-ai/d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
microsoft/nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
optuna/optuna
A hyperparameter optimization framework
wandb/wandb
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
EpistasisLab/tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
autogluon/autogluon
Fast and Accurate ML in 3 Lines of Code
automl/auto-sklearn
Automated Machine Learning with scikit-learn
microsoft/FLAML
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
hibayesian/awesome-automl-papers
A curated list of automated machine learning papers, articles, tutorials, slides and projects
amanchadha/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
polyaxon/polyaxon
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
mljar/mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
determined-ai/determined
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
keras-team/keras-tuner
A Hyperparameter Tuning Library for Keras
scikit-optimize/scikit-optimize
Sequential model-based optimization with a `scipy.optimize` interface
DLR-RM/rl-baselines3-zoo
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
maxpumperla/hyperas
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
ClimbsRocks/auto_ml
[UNMAINTAINED] Automated machine learning for analytics & production
autonomio/talos
Hyperparameter Experiments with TensorFlow and Keras
google/vizier
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
facebookexperimental/Robyn
Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
reiinakano/xcessiv
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
SimonBlanke/Gradient-Free-Optimizers
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
automl/SMAC3
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
araffin/rl-baselines-zoo
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
guan-yuan/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.
AgileRL/AgileRL
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.
optuna/optuna-examples
Examples for https://github.com/optuna/optuna
MIND-Lab/OCTIS
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
williamFalcon/test-tube
Python library to easily log experiments and parallelize hyperparameter search for neural networks
HunterMcGushion/hyperparameter_hunter
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
aimclub/FEDOT
Automated modeling and machine learning framework FEDOT
optuna/optuna-dashboard
Real-time Web Dashboard for Optuna.
automl/HpBandSter
a distributed Hyperband implementation on Steroids