A Brief Introduction to Automated Machine Learning (AutoML) скачать в хорошем качестве

A Brief Introduction to Automated Machine Learning (AutoML)

This video introduces the topic of Automated Machine Learning (AutoML) for beginners looking to understand or select an AutoML tool or library. This video (1) provides background information on machine learning and data science pipelines, (2) defines AutoML and major differences between AutoML tools and libraries, and (3) includes a survey of 24 open source AutoML solutions as of December 2023 aimed at helping potential users select an AutoML solution that might best work for their needs. Video Links: Urbanowicz et. al 2023 - Supporting information: https://arxiv.org/abs/2312.05461 STREAMLINE GitHub: https://github.com/UrbsLab/STREAMLINE Auto-Keras GitHub: https://github.com/keras-team/autokeras H20-3 AutoML GitHub: https://github.com/h2oai/h2o-3 MLme GitHub: https://github.com/FunctionalUrology/... LAMA GitHub: https://github.com/sb-ai-lab/LightAutoML FEDOT GitHub: https://github.com/aimclub/FEDOT FLAML GitHub: https://github.com/microsoft/FLAML ALIRO GitHub: https://github.com/EpistasisLab/Aliro PYCARET GitHub: https://github.com/pycaret/pycaret MLJAR-Supervised GitHub: https://github.com/mljar/mljar-superv... Ludwig GitHub: https://github.com/ludwig-ai/ludwig/ TPOT GitHub: https://github.com/epistasislab/tpot/ Auto-Sklearn GitHub: https://github.com/automl/auto-sklearn Auto-PyTorch GitHub: https://github.com/automl/Auto-PyTorch GAMA GitHub: https://github.com/openml-labs/gama Hyperopt-sklearn GitHub: https://github.com/hyperopt/hyperopt-... Auto-WEKA GitHub: https://github.com/automl/autoweka RECIPE GitHub: https://github.com/laic-ufmg/Recipe ML-Plan GitHub: https://github.com/starlibs/AILibs TransmogrifAI GitHub: https://github.com/salesforce/Transmo... MLBox GitHub: https://github.com/AxeldeRomblay/MLBox Xcessiv GitHub: https://github.com/reiinakano/xcessiv Auto_ML GitHub: https://github.com/ClimbsRocks/auto_ml Other Weblinks: https://github.com/UrbsLab http://ryanurbanowicz.com/ Chapters: 0:00 Introduction and Machine Learning Background 10:24 Automated Machine Learning (AutoML) Basics 12:50 Survey of AutoML Tools/Libraries 15:52 Considerations for Choosing an AutoML Solution 26:18 Summary