Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY
Apress
2023
|
| Subjects: | |
| Links: | https://learning.oreilly.com/library/view/-/9781484290637/?ar |
| Summary: | This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization. |
| Item Description: | Includes index. - Print version record |
| Physical Description: | 1 Online-Ressource (xv, 234 pages) illustrations (black and white, and colour). |
| ISBN: | 1484290631 9781484290637 |
Staff View
MARC
| LEADER | 00000cam a22000002c 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-092523161 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228121940.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230503s2023 xx |||||o 00| ||eng c | ||
| 020 | |a 1484290631 |c electronic book |9 1-4842-9063-1 | ||
| 020 | |a 9781484290637 |c electronic bk. |9 978-1-4842-9063-7 | ||
| 035 | |a (DE-627-1)092523161 | ||
| 035 | |a (DE-599)KEP092523161 | ||
| 035 | |a (ORHE)9781484290637 | ||
| 035 | |a (DE-627-1)092523161 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 072 | 7 | |a UYQM |2 bicssc | |
| 072 | 7 | |a COM004000 |2 bisacsh | |
| 082 | 0 | |a 519.5/42 |2 23/eng/20230329 | |
| 100 | 1 | |a Liu, Peng |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Bayesian optimization |b theory and practice using Python |c Peng Liu |
| 264 | 1 | |a New York, NY |b Apress |c 2023 | |
| 300 | |a 1 Online-Ressource (xv, 234 pages) |b illustrations (black and white, and colour). | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. - Print version record | ||
| 520 | |a This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization. | ||
| 650 | 0 | |a Bayesian statistical decision theory |x Data processing | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Mathematical optimization | |
| 650 | 4 | |a Théorie de la décision bayésienne ; Informatique | |
| 650 | 4 | |a Python (Langage de programmation) | |
| 650 | 4 | |a Optimisation mathématique | |
| 650 | 4 | |a Bayesian statistical decision theory ; Data processing | |
| 650 | 4 | |a Mathematical optimization | |
| 650 | 4 | |a Python (Computer program language) | |
| 776 | 1 | |z 1484290623 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 1484290623 |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781484290637/?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 | ||
Record in the Search Index
| DE-BY-TUM_katkey | ZDB-30-ORH-092523161 |
|---|---|
| _version_ | 1835903130930249728 |
| adam_text | |
| any_adam_object | |
| author | Liu, Peng |
| author_facet | Liu, Peng |
| author_role | aut |
| author_sort | Liu, Peng |
| author_variant | p l pl |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)092523161 (DE-599)KEP092523161 (ORHE)9781484290637 |
| dewey-full | 519.5/42 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/42 |
| dewey-search | 519.5/42 |
| dewey-sort | 3519.5 242 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02178cam a22005052c 4500</leader><controlfield tag="001">ZDB-30-ORH-092523161</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121940.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230503s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1484290631</subfield><subfield code="c">electronic book</subfield><subfield code="9">1-4842-9063-1</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781484290637</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-4842-9063-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)092523161</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP092523161</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781484290637</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)092523161</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="072" ind1=" " ind2="7"><subfield code="a">UYQM</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM004000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5/42</subfield><subfield code="2">23/eng/20230329</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Peng</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian optimization</subfield><subfield code="b">theory and practice using Python</subfield><subfield code="c">Peng Liu</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Apress</subfield><subfield code="c">2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xv, 234 pages)</subfield><subfield code="b">illustrations (black and white, and colour).</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">Includes index. - Print version record</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Bayesian statistical decision theory</subfield><subfield code="x">Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Mathematical optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Théorie de la décision bayésienne ; Informatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimisation mathématique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian statistical decision theory ; Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">1484290623</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">1484290623</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/-/9781484290637/?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-092523161 |
| illustrated | Illustrated |
| indexdate | 2025-06-25T12:14:23Z |
| institution | BVB |
| isbn | 1484290631 9781484290637 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xv, 234 pages) illustrations (black and white, and colour). |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Apress |
| record_format | marc |
| spelling | Liu, Peng VerfasserIn aut Bayesian optimization theory and practice using Python Peng Liu New York, NY Apress 2023 1 Online-Ressource (xv, 234 pages) illustrations (black and white, and colour). Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes index. - Print version record This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization. Bayesian statistical decision theory Data processing Python (Computer program language) Mathematical optimization Théorie de la décision bayésienne ; Informatique Python (Langage de programmation) Optimisation mathématique Bayesian statistical decision theory ; Data processing 1484290623 Erscheint auch als Druck-Ausgabe 1484290623 |
| spellingShingle | Liu, Peng Bayesian optimization theory and practice using Python Bayesian statistical decision theory Data processing Python (Computer program language) Mathematical optimization Théorie de la décision bayésienne ; Informatique Python (Langage de programmation) Optimisation mathématique Bayesian statistical decision theory ; Data processing |
| title | Bayesian optimization theory and practice using Python |
| title_auth | Bayesian optimization theory and practice using Python |
| title_exact_search | Bayesian optimization theory and practice using Python |
| title_full | Bayesian optimization theory and practice using Python Peng Liu |
| title_fullStr | Bayesian optimization theory and practice using Python Peng Liu |
| title_full_unstemmed | Bayesian optimization theory and practice using Python Peng Liu |
| title_short | Bayesian optimization |
| title_sort | bayesian optimization theory and practice using python |
| title_sub | theory and practice using Python |
| topic | Bayesian statistical decision theory Data processing Python (Computer program language) Mathematical optimization Théorie de la décision bayésienne ; Informatique Python (Langage de programmation) Optimisation mathématique Bayesian statistical decision theory ; Data processing |
| topic_facet | Bayesian statistical decision theory Data processing Python (Computer program language) Mathematical optimization Théorie de la décision bayésienne ; Informatique Python (Langage de programmation) Optimisation mathématique Bayesian statistical decision theory ; Data processing |
| work_keys_str_mv | AT liupeng bayesianoptimizationtheoryandpracticeusingpython |