Using AutoML to automate selection of machine learning models and hyperparameters:
"Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. In recent years, AutoML has fostered a fundamental shift in how organizations approach machine learning, making it more accessible to bot...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | |
| Format: | Elektronisch Video |
| Sprache: | Englisch |
| Veröffentlicht: |
[Place of publication not identified]
O'Reilly
2019
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/0636920339601/?ar |
| Zusammenfassung: | "Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. In recent years, AutoML has fostered a fundamental shift in how organizations approach machine learning, making it more accessible to both experts and nonexperts. Most real-world data science projects are time-consuming, resource intensive, and challenging. Besides data preparation, data cleaning, and feature engineering, data scientists often spend a significant amount of time on model selection and tuning of hyperparameters. Automated machine learning changes that, making it easier to build and use machine learning models in the real world. Francesca Lazzeri and Wee Hyong Tok (Microsoft) lead a gentle introduction to how AutoML works and the state-of-art AutoML capabilities that are available. You'll learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters."--Resource description page |
| Beschreibung: | Title from title screen (viewed November 14, 2019) |
| Umfang: | 1 Online-Ressource (1 streaming video file (42 min., 33 sec.)) |
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| spelling | Lazzeri, Francesca VerfasserIn aut Using AutoML to automate selection of machine learning models and hyperparameters Francesca Lazzeri, Wee Hyong Tok [Place of publication not identified] O'Reilly 2019 1 Online-Ressource (1 streaming video file (42 min., 33 sec.)) zweidimensionales bewegtes Bild tdi rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Title from title screen (viewed November 14, 2019) "Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. In recent years, AutoML has fostered a fundamental shift in how organizations approach machine learning, making it more accessible to both experts and nonexperts. Most real-world data science projects are time-consuming, resource intensive, and challenging. Besides data preparation, data cleaning, and feature engineering, data scientists often spend a significant amount of time on model selection and tuning of hyperparameters. Automated machine learning changes that, making it easier to build and use machine learning models in the real world. Francesca Lazzeri and Wee Hyong Tok (Microsoft) lead a gentle introduction to how AutoML works and the state-of-art AutoML capabilities that are available. You'll learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters."--Resource description page Machine learning Electronic data processing Apprentissage automatique Electronic data processing (OCoLC)fst00906956 Machine learning (OCoLC)fst01004795 Tok, Wee-Hyong MitwirkendeR ctb |
| spellingShingle | Lazzeri, Francesca Using AutoML to automate selection of machine learning models and hyperparameters Machine learning Electronic data processing Apprentissage automatique Electronic data processing (OCoLC)fst00906956 Machine learning (OCoLC)fst01004795 |
| subject_GND | (OCoLC)fst00906956 (OCoLC)fst01004795 |
| title | Using AutoML to automate selection of machine learning models and hyperparameters |
| title_auth | Using AutoML to automate selection of machine learning models and hyperparameters |
| title_exact_search | Using AutoML to automate selection of machine learning models and hyperparameters |
| title_full | Using AutoML to automate selection of machine learning models and hyperparameters Francesca Lazzeri, Wee Hyong Tok |
| title_fullStr | Using AutoML to automate selection of machine learning models and hyperparameters Francesca Lazzeri, Wee Hyong Tok |
| title_full_unstemmed | Using AutoML to automate selection of machine learning models and hyperparameters Francesca Lazzeri, Wee Hyong Tok |
| title_short | Using AutoML to automate selection of machine learning models and hyperparameters |
| title_sort | using automl to automate selection of machine learning models and hyperparameters |
| topic | Machine learning Electronic data processing Apprentissage automatique Electronic data processing (OCoLC)fst00906956 Machine learning (OCoLC)fst01004795 |
| topic_facet | Machine learning Electronic data processing Apprentissage automatique |
| work_keys_str_mv | AT lazzerifrancesca usingautomltoautomateselectionofmachinelearningmodelsandhyperparameters AT tokweehyong usingautomltoautomateselectionofmachinelearningmodelsandhyperparameters |