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...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Electronic Video |
| Language: | English |
| Published: |
[Place of publication not identified]
O'Reilly
2019
|
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/0636920339601/?ar |
| Summary: | "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 |
| Item Description: | Title from title screen (viewed November 14, 2019) |
| Physical Description: | 1 Online-Ressource (1 streaming video file (42 min., 33 sec.)) |
Staff View
MARC
| LEADER | 00000cgm a22000002c 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-048623857 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228120917.0 | ||
| 006 | m o | | | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191206s2019 xx ||| |o o ||eng c | ||
| 035 | |a (DE-627-1)048623857 | ||
| 035 | |a (DE-599)KEP048623857 | ||
| 035 | |a (ORHE)0636920339601 | ||
| 035 | |a (DE-627-1)048623857 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 100 | 1 | |a Lazzeri, Francesca |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Using AutoML to automate selection of machine learning models and hyperparameters |c Francesca Lazzeri, Wee Hyong Tok |
| 264 | 1 | |a [Place of publication not identified] |b O'Reilly |c 2019 | |
| 300 | |a 1 Online-Ressource (1 streaming video file (42 min., 33 sec.)) | ||
| 336 | |a zweidimensionales bewegtes Bild |b tdi |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Title from title screen (viewed November 14, 2019) | ||
| 520 | |a "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 | ||
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Electronic data processing | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Electronic data processing |0 (OCoLC)fst00906956 | |
| 650 | 4 | |a Machine learning |0 (OCoLC)fst01004795 | |
| 700 | 1 | |a Tok, Wee-Hyong |e MitwirkendeR |4 ctb | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/0636920339601/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 935 | |c vide | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Record in the Search Index
| DE-BY-TUM_katkey | ZDB-30-ORH-048623857 |
|---|---|
| _version_ | 1829007853639172096 |
| adam_text | |
| any_adam_object | |
| author | Lazzeri, Francesca |
| author2 | Tok, Wee-Hyong |
| author2_role | ctb |
| author2_variant | w h t wht |
| author_facet | Lazzeri, Francesca Tok, Wee-Hyong |
| author_role | aut |
| author_sort | Lazzeri, Francesca |
| author_variant | f l fl |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)048623857 (DE-599)KEP048623857 (ORHE)0636920339601 |
| format | Electronic Video |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02467cgm a22003972c 4500</leader><controlfield tag="001">ZDB-30-ORH-048623857</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228120917.0</controlfield><controlfield tag="006">m o | | </controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191206s2019 xx ||| |o o ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)048623857</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP048623857</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)0636920339601</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)048623857</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lazzeri, Francesca</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using AutoML to automate selection of machine learning models and hyperparameters</subfield><subfield code="c">Francesca Lazzeri, Wee Hyong Tok</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">[Place of publication not identified]</subfield><subfield code="b">O'Reilly</subfield><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1 streaming video file (42 min., 33 sec.))</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">zweidimensionales bewegtes Bild</subfield><subfield code="b">tdi</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Title from title screen (viewed November 14, 2019)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">"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</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Electronic data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electronic data processing</subfield><subfield code="0">(OCoLC)fst00906956</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="0">(OCoLC)fst01004795</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tok, Wee-Hyong</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/0636920339601/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="935" ind1=" " ind2=" "><subfield code="c">vide</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-048623857 |
| illustrated | Not Illustrated |
| indexdate | 2025-04-10T09:36:54Z |
| institution | BVB |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (1 streaming video file (42 min., 33 sec.)) |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | O'Reilly |
| record_format | marc |
| 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 |