Understanding support vector machines:
What you'll learn-and how you can apply it You'll learn the core concepts one of the most popular models in Machine Learning-support vector machines-how to use them, and how they work. Readers will gain an intuitive understanding of the mathematics involved in SVMs, including an introducti...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Place of publication not identified]
O'Reilly
[2017]
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781491978733/?ar |
| Zusammenfassung: | What you'll learn-and how you can apply it You'll learn the core concepts one of the most popular models in Machine Learning-support vector machines-how to use them, and how they work. Readers will gain an intuitive understanding of the mathematics involved in SVMs, including an introduction to using polynomial kernels. At the end of this Lesson, readers will be able to do binary classification for rather simple problems. This lesson is for you because You have some programming experience and you're ready to code a Machine Learning project. You want to classify attributes on small- to medium-sized datasets and possibly complex datasets. Prerequisites: Have some programming experience (know how to code in Python) Understanding of basic machine learning concepts (fitting a model to data) Materials or downloads needed: Python Scikit-Learn (code written and tested on v. 0.18). |
| Beschreibung: | "From Hands-on machine learning with Scikit-Learn and TensorFlow by Aurélien Géron"--Cover. - Date of publication from resource description page. - Online resource; title from title page (Safari, viewed May 25, 2017) |
| Umfang: | 1 Online-Ressource (1 volume) illustrations |
| ISBN: | 9781491978733 1491978732 |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-047620331 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228120302.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2017 xx |||||o 00| ||eng c | ||
| 020 | |a 9781491978733 |9 978-1-4919-7873-3 | ||
| 020 | |a 1491978732 |9 1-4919-7873-2 | ||
| 035 | |a (DE-627-1)047620331 | ||
| 035 | |a (DE-599)KEP047620331 | ||
| 035 | |a (ORHE)9781491978733 | ||
| 035 | |a (DE-627-1)047620331 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a [E] | |
| 100 | 1 | |a Géron, Aurélien |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Understanding support vector machines |c Aurélien Géron |
| 264 | 1 | |a [Place of publication not identified] |b O'Reilly |c [2017] | |
| 300 | |a 1 Online-Ressource (1 volume) |b illustrations | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a "From Hands-on machine learning with Scikit-Learn and TensorFlow by Aurélien Géron"--Cover. - Date of publication from resource description page. - Online resource; title from title page (Safari, viewed May 25, 2017) | ||
| 520 | |a What you'll learn-and how you can apply it You'll learn the core concepts one of the most popular models in Machine Learning-support vector machines-how to use them, and how they work. Readers will gain an intuitive understanding of the mathematics involved in SVMs, including an introduction to using polynomial kernels. At the end of this Lesson, readers will be able to do binary classification for rather simple problems. This lesson is for you because You have some programming experience and you're ready to code a Machine Learning project. You want to classify attributes on small- to medium-sized datasets and possibly complex datasets. Prerequisites: Have some programming experience (know how to code in Python) Understanding of basic machine learning concepts (fitting a model to data) Materials or downloads needed: Python Scikit-Learn (code written and tested on v. 0.18). | ||
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Artificial intelligence | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Machine Learning | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Intelligence artificielle | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Machine learning | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781491978733/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-047620331 |
|---|---|
| _version_ | 1821494872117346304 |
| adam_text | |
| any_adam_object | |
| author | Géron, Aurélien |
| author_facet | Géron, Aurélien |
| author_role | aut |
| author_sort | Géron, Aurélien |
| author_variant | a g ag |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)047620331 (DE-599)KEP047620331 (ORHE)9781491978733 |
| dewey-raw | [E] |
| dewey-search | [E] |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02557cam a22004572 4500</leader><controlfield tag="001">ZDB-30-ORH-047620331</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228120302.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781491978733</subfield><subfield code="9">978-1-4919-7873-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1491978732</subfield><subfield code="9">1-4919-7873-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047620331</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP047620331</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781491978733</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)047620331</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="082" ind1="0" ind2=" "><subfield code="a">[E]</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Géron, Aurélien</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Understanding support vector machines</subfield><subfield code="c">Aurélien Géron</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">[2017]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1 volume)</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</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">"From Hands-on machine learning with Scikit-Learn and TensorFlow by Aurélien Géron"--Cover. - Date of publication from resource description page. - Online resource; title from title page (Safari, viewed May 25, 2017)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">What you'll learn-and how you can apply it You'll learn the core concepts one of the most popular models in Machine Learning-support vector machines-how to use them, and how they work. Readers will gain an intuitive understanding of the mathematics involved in SVMs, including an introduction to using polynomial kernels. At the end of this Lesson, readers will be able to do binary classification for rather simple problems. This lesson is for you because You have some programming experience and you're ready to code a Machine Learning project. You want to classify attributes on small- to medium-sized datasets and possibly complex datasets. Prerequisites: Have some programming experience (know how to code in Python) Understanding of basic machine learning concepts (fitting a model to data) Materials or downloads needed: Python Scikit-Learn (code written and tested on v. 0.18).</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Artificial Intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Machine Learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligence artificielle</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</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/-/9781491978733/?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="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</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-047620331 |
| illustrated | Illustrated |
| indexdate | 2025-01-17T11:21:16Z |
| institution | BVB |
| isbn | 9781491978733 1491978732 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (1 volume) illustrations |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | O'Reilly |
| record_format | marc |
| spelling | Géron, Aurélien VerfasserIn aut Understanding support vector machines Aurélien Géron [Place of publication not identified] O'Reilly [2017] 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier "From Hands-on machine learning with Scikit-Learn and TensorFlow by Aurélien Géron"--Cover. - Date of publication from resource description page. - Online resource; title from title page (Safari, viewed May 25, 2017) What you'll learn-and how you can apply it You'll learn the core concepts one of the most popular models in Machine Learning-support vector machines-how to use them, and how they work. Readers will gain an intuitive understanding of the mathematics involved in SVMs, including an introduction to using polynomial kernels. At the end of this Lesson, readers will be able to do binary classification for rather simple problems. This lesson is for you because You have some programming experience and you're ready to code a Machine Learning project. You want to classify attributes on small- to medium-sized datasets and possibly complex datasets. Prerequisites: Have some programming experience (know how to code in Python) Understanding of basic machine learning concepts (fitting a model to data) Materials or downloads needed: Python Scikit-Learn (code written and tested on v. 0.18). Machine learning Artificial intelligence Artificial Intelligence Machine Learning Apprentissage automatique Intelligence artificielle artificial intelligence |
| spellingShingle | Géron, Aurélien Understanding support vector machines Machine learning Artificial intelligence Artificial Intelligence Machine Learning Apprentissage automatique Intelligence artificielle artificial intelligence |
| title | Understanding support vector machines |
| title_auth | Understanding support vector machines |
| title_exact_search | Understanding support vector machines |
| title_full | Understanding support vector machines Aurélien Géron |
| title_fullStr | Understanding support vector machines Aurélien Géron |
| title_full_unstemmed | Understanding support vector machines Aurélien Géron |
| title_short | Understanding support vector machines |
| title_sort | understanding support vector machines |
| topic | Machine learning Artificial intelligence Artificial Intelligence Machine Learning Apprentissage automatique Intelligence artificielle artificial intelligence |
| topic_facet | Machine learning Artificial intelligence Artificial Intelligence Machine Learning Apprentissage automatique Intelligence artificielle artificial intelligence |
| work_keys_str_mv | AT geronaurelien understandingsupportvectormachines |