#Hyperparameter-optimization
Showing 60 of 79 repositories tagged #hyperparameter-optimization, ranked by stars
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
A hyperparameter optimization framework
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Fast and Accurate ML in 3 Lines of Code
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Automated Machine Learning with scikit-learn
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
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
AI Infra / AI Orchestration / AI Control Plane
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
A Hyperparameter Tuning Library for Keras
Sequential model-based optimization with a `scipy.optimize` interface
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
[UNMAINTAINED] Automated machine learning for analytics & production
Hyperparameter Experiments with TensorFlow and Keras
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
HyperView is a terminal-first TradingView strategy lab for downloading market data, backtesting Python strategies with Pine-like behavior, and optimizing SL/TP parameters.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.
Examples for https://github.com/optuna/optuna
Python library to easily log experiments and parallelize hyperparameter search for neural networks
Automated modeling and machine learning framework FEDOT
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
Tuning hyperparams fast with Hyperband
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Notes for Deep Learning Specialization Courses led by Andrew Ng.
A unified interface for optimization algorithms and experiments
Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models
A curated list of awesome Distributed Deep Learning resources.
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
Parallel Hyperparameter Tuning in Python
本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢
Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.
Library for Semi-Automated Data Science
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
syftr is an agent optimizer that helps you find the best agentic workflows for your budget.
Autonomous Performance Tuning for Kubernetes!
Time Series Cross-Validation -- an extension for scikit-learn
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Clojure machine learning library
State-of-the art Automated Machine Learning python library for Tabular Data
Simplifying the definition and execution, scaling and deployment of pipelines on the cloud.
An automatic ML model optimization tool.
Code repository for the online course Hyperparameter Optimization for Machine Learning
Universal Python SDK to run AI workloads on Kubernetes
Streamlined machine learning experiment management.
Interactive coding assistant for data scientists and machine learning developers, empowered by large language models.
Simple, but essential Bayesian optimization package
Python library for Bayesian hyper-parameters optimization
A convenient way to trigger synchronizations to wandb / Weights & Biases if your compute nodes don't have internet!
Hyperparameter tuning for machine learning models using a distributed genetic algorithm
[NeurIPS 2021] Well-tuned Simple Nets Excel on Tabular Datasets
Metadata store for Production ML
PyHopper is a hyperparameter optimizer, made specifically for high-dimensional problems arising in machine learning research.