Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
-
Updated
Aug 27, 2022 - Python
{{ message }}
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
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.
A hyperparameter optimization framework
Automated Machine Learning with scikit-learn
AutoGluon: AutoML for Image, Text, and Tabular Data
A curated list of automated machine learning papers, articles, tutorials, slides and projects
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Sequential model-based optimization with a `scipy.optimize` interface
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
A fast library for AutoML and tuning.
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Determined: Deep Learning Training Platform
[UNMAINTAINED] Automated machine learning for analytics & production
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
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
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Add a description, image, and links to the hyperparameter-optimization topic page so that developers can more easily learn about it.
To associate your repository with the hyperparameter-optimization topic, visit your repo's landing page and select "manage topics."