TensorFlow Pocket Primer:
As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to TensorFlow 1.x fundamentals for basic machine learning algorithms in TensorFlow. It is intended to be a fast-paced introduction to various "core" features of TensorFlow, with code samples that...
Gespeichert in:
1. Verfasser: | |
---|---|
Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Dulles, VA
Mercury Learning and Information
[2019]
|
Schriftenreihe: | Pocket Primer
|
Schlagworte: | |
Online-Zugang: | DE-1046 DE-1043 DE-858 DE-859 DE-860 DE-739 DE-Aug4 Volltext |
Zusammenfassung: | As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to TensorFlow 1.x fundamentals for basic machine learning algorithms in TensorFlow. It is intended to be a fast-paced introduction to various "core" features of TensorFlow, with code samples that cover deep learning and TensorFlow basics. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by writing to info@merclearning.com. Features: Uses Python for code samplesCovers TensorFlow APIs and DatasetsAssumes the reader has very limited experienceCompanion files with all of the source code examples (download from the publisher) |
Beschreibung: | Description based on online resource; title from PDF title page (publisher's Web site, viewed 26. Mrz 2024) |
Beschreibung: | 1 Online-Ressource (152 Seiten) |
ISBN: | 9781683923664 |
DOI: | 10.1515/9781683923664 |
Internformat
MARC
LEADER | 00000nmm a2200000zc 4500 | ||
---|---|---|---|
001 | BV049653213 | ||
003 | DE-604 | ||
007 | cr|uuu---uuuuu | ||
008 | 240417s2019 |||| o||u| ||||||eng d | ||
020 | |a 9781683923664 |9 978-1-68392-366-4 | ||
024 | 7 | |a 10.1515/9781683923664 |2 doi | |
035 | |a (ZDB-23-DGG)9781683923664 | ||
035 | |a (OCoLC)1430764240 | ||
035 | |a (DE-599)BVBBV049653213 | ||
040 | |a DE-604 |b ger |e rda | ||
041 | 0 | |a eng | |
049 | |a DE-1043 |a DE-1046 |a DE-858 |a DE-Aug4 |a DE-859 |a DE-860 |a DE-739 | ||
082 | 0 | |a 005 |2 22//eng/20230216eng | |
100 | 1 | |a Campesato, Oswald |e Verfasser |4 aut | |
245 | 1 | 0 | |a TensorFlow Pocket Primer |c Oswald Campesato |
264 | 1 | |a Dulles, VA |b Mercury Learning and Information |c [2019] | |
264 | 4 | |c © 2019 | |
300 | |a 1 Online-Ressource (152 Seiten) | ||
336 | |b txt |2 rdacontent | ||
337 | |b c |2 rdamedia | ||
338 | |b cr |2 rdacarrier | ||
490 | 0 | |a Pocket Primer | |
500 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 26. Mrz 2024) | ||
520 | |a As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to TensorFlow 1.x fundamentals for basic machine learning algorithms in TensorFlow. It is intended to be a fast-paced introduction to various "core" features of TensorFlow, with code samples that cover deep learning and TensorFlow basics. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by writing to info@merclearning.com. Features: Uses Python for code samplesCovers TensorFlow APIs and DatasetsAssumes the reader has very limited experienceCompanion files with all of the source code examples (download from the publisher) | ||
546 | |a In English | ||
650 | 4 | |a Programming | |
650 | 7 | |a COMPUTERS / Programming Languages / Python |2 bisacsh | |
776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781683923640 |
856 | 4 | 0 | |u https://doi.org/10.1515/9781683923664 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
912 | |a ZDB-23-DGG | ||
966 | e | |u https://doi.org/10.1515/9781683923664 |l DE-1046 |p ZDB-23-DGG |q FAW_PDA_DGG |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1515/9781683923664 |l DE-1043 |p ZDB-23-DGG |q FAB_PDA_DGG |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1515/9781683923664 |l DE-858 |p ZDB-23-DGG |q FCO_PDA_DGG |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1515/9781683923664 |l DE-859 |p ZDB-23-DGG |q FKE_PDA_DGG |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1515/9781683923664 |l DE-860 |p ZDB-23-DGG |q FLA_PDA_DGG |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1515/9781683923664 |l DE-739 |p ZDB-23-DGG |q UPA_PDA_DGG |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1515/9781683923664 |l DE-Aug4 |p ZDB-23-DGG |q FHA_PDA_DGG |x Verlag |3 Volltext |
Datensatz im Suchindex
_version_ | 1805071459042721792 |
---|---|
adam_text | |
adam_txt | |
any_adam_object | |
any_adam_object_boolean | |
author | Campesato, Oswald |
author_facet | Campesato, Oswald |
author_role | aut |
author_sort | Campesato, Oswald |
author_variant | o c oc |
building | Verbundindex |
bvnumber | BV049653213 |
collection | ZDB-23-DGG |
ctrlnum | (ZDB-23-DGG)9781683923664 (OCoLC)1430764240 (DE-599)BVBBV049653213 |
dewey-full | 005 |
dewey-hundreds | 000 - Computer science, information, general works |
dewey-ones | 005 - Computer programming, programs, data, security |
dewey-raw | 005 |
dewey-search | 005 |
dewey-sort | 15 |
dewey-tens | 000 - Computer science, information, general works |
discipline | Informatik |
discipline_str_mv | Informatik |
doi_str_mv | 10.1515/9781683923664 |
format | Electronic eBook |
fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nmm a2200000zc 4500</leader><controlfield tag="001">BV049653213</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">240417s2019 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781683923664</subfield><subfield code="9">978-1-68392-366-4</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1515/9781683923664</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-23-DGG)9781683923664</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1430764240</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049653213</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1043</subfield><subfield code="a">DE-1046</subfield><subfield code="a">DE-858</subfield><subfield code="a">DE-Aug4</subfield><subfield code="a">DE-859</subfield><subfield code="a">DE-860</subfield><subfield code="a">DE-739</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">005</subfield><subfield code="2">22//eng/20230216eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Campesato, Oswald</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">TensorFlow Pocket Primer</subfield><subfield code="c">Oswald Campesato</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Dulles, VA</subfield><subfield code="b">Mercury Learning and Information</subfield><subfield code="c">[2019]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (152 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Pocket Primer</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on online resource; title from PDF title page (publisher's Web site, viewed 26. Mrz 2024)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to TensorFlow 1.x fundamentals for basic machine learning algorithms in TensorFlow. It is intended to be a fast-paced introduction to various "core" features of TensorFlow, with code samples that cover deep learning and TensorFlow basics. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by writing to info@merclearning.com. Features: Uses Python for code samplesCovers TensorFlow APIs and DatasetsAssumes the reader has very limited experienceCompanion files with all of the source code examples (download from the publisher)</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">In English</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Programming</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Programming Languages / Python</subfield><subfield code="2">bisacsh</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">9781683923640</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1515/9781683923664</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-23-DGG</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9781683923664</subfield><subfield code="l">DE-1046</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FAW_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9781683923664</subfield><subfield code="l">DE-1043</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FAB_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9781683923664</subfield><subfield code="l">DE-858</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FCO_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9781683923664</subfield><subfield code="l">DE-859</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FKE_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9781683923664</subfield><subfield code="l">DE-860</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FLA_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9781683923664</subfield><subfield code="l">DE-739</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">UPA_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1515/9781683923664</subfield><subfield code="l">DE-Aug4</subfield><subfield code="p">ZDB-23-DGG</subfield><subfield code="q">FHA_PDA_DGG</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
id | DE-604.BV049653213 |
illustrated | Not Illustrated |
index_date | 2024-07-03T23:40:32Z |
indexdate | 2024-07-20T04:38:09Z |
institution | BVB |
isbn | 9781683923664 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034996616 |
oclc_num | 1430764240 |
open_access_boolean | |
owner | DE-1043 DE-1046 DE-858 DE-Aug4 DE-859 DE-860 DE-739 |
owner_facet | DE-1043 DE-1046 DE-858 DE-Aug4 DE-859 DE-860 DE-739 |
physical | 1 Online-Ressource (152 Seiten) |
psigel | ZDB-23-DGG ZDB-23-DGG FAW_PDA_DGG ZDB-23-DGG FAB_PDA_DGG ZDB-23-DGG FCO_PDA_DGG ZDB-23-DGG FKE_PDA_DGG ZDB-23-DGG FLA_PDA_DGG ZDB-23-DGG UPA_PDA_DGG ZDB-23-DGG FHA_PDA_DGG |
publishDate | 2019 |
publishDateSearch | 2019 |
publishDateSort | 2019 |
publisher | Mercury Learning and Information |
record_format | marc |
series2 | Pocket Primer |
spelling | Campesato, Oswald Verfasser aut TensorFlow Pocket Primer Oswald Campesato Dulles, VA Mercury Learning and Information [2019] © 2019 1 Online-Ressource (152 Seiten) txt rdacontent c rdamedia cr rdacarrier Pocket Primer Description based on online resource; title from PDF title page (publisher's Web site, viewed 26. Mrz 2024) As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to TensorFlow 1.x fundamentals for basic machine learning algorithms in TensorFlow. It is intended to be a fast-paced introduction to various "core" features of TensorFlow, with code samples that cover deep learning and TensorFlow basics. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by writing to info@merclearning.com. Features: Uses Python for code samplesCovers TensorFlow APIs and DatasetsAssumes the reader has very limited experienceCompanion files with all of the source code examples (download from the publisher) In English Programming COMPUTERS / Programming Languages / Python bisacsh Erscheint auch als Druck-Ausgabe 9781683923640 https://doi.org/10.1515/9781683923664 Verlag URL des Erstveröffentlichers Volltext |
spellingShingle | Campesato, Oswald TensorFlow Pocket Primer Programming COMPUTERS / Programming Languages / Python bisacsh |
title | TensorFlow Pocket Primer |
title_auth | TensorFlow Pocket Primer |
title_exact_search | TensorFlow Pocket Primer |
title_exact_search_txtP | TensorFlow Pocket Primer |
title_full | TensorFlow Pocket Primer Oswald Campesato |
title_fullStr | TensorFlow Pocket Primer Oswald Campesato |
title_full_unstemmed | TensorFlow Pocket Primer Oswald Campesato |
title_short | TensorFlow Pocket Primer |
title_sort | tensorflow pocket primer |
topic | Programming COMPUTERS / Programming Languages / Python bisacsh |
topic_facet | Programming COMPUTERS / Programming Languages / Python |
url | https://doi.org/10.1515/9781683923664 |
work_keys_str_mv | AT campesatooswald tensorflowpocketprimer |