TensorFlow Pocket Primer / / Oswald Campesato.

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 dee...

Full description

Saved in:
Bibliographic Details
VerfasserIn:
Place / Publishing House:Dulles, VA : : Mercury Learning and Information, , [2019]
©2019
Year of Publication:2019
Language:English
Series:Pocket Primer
Online Access:
Physical Description:1 online resource (152 p.)
Tags: Add Tag
No Tags, Be the first to tag this record!
id 9781683923664
ctrlnum (DE-B1597)672136
collection bib_alma
record_format marc
spelling Campesato, Oswald, author. aut http://id.loc.gov/vocabulary/relators/aut
TensorFlow Pocket Primer / Oswald Campesato.
Dulles, VA : Mercury Learning and Information, [2019]
©2019
1 online resource (152 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Pocket Primer
restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star
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)
Issued also in print.
Mode of access: Internet via World Wide Web.
In English.
Description based on online resource; title from PDF title page (publisher's Web site, viewed 26. Mrz 2024)
Programming.
COMPUTERS / Programming Languages / Python. bisacsh
EPUB 9781683923657
print 9781683923640
https://doi.org/10.1515/9781683923664
https://www.degruyter.com/isbn/9781683923664
Cover https://www.degruyter.com/document/cover/isbn/9781683923664/original
language English
format eBook
author Campesato, Oswald,
Campesato, Oswald,
spellingShingle Campesato, Oswald,
Campesato, Oswald,
TensorFlow Pocket Primer /
Pocket Primer
author_facet Campesato, Oswald,
Campesato, Oswald,
author_variant o c oc
o c oc
author_role VerfasserIn
VerfasserIn
author_sort Campesato, Oswald,
title 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_auth TensorFlow Pocket Primer /
title_new TensorFlow Pocket Primer /
title_sort tensorflow pocket primer /
series Pocket Primer
series2 Pocket Primer
publisher Mercury Learning and Information,
publishDate 2019
physical 1 online resource (152 p.)
Issued also in print.
isbn 9781683923664
9781683923657
9781683923640
url https://doi.org/10.1515/9781683923664
https://www.degruyter.com/isbn/9781683923664
https://www.degruyter.com/document/cover/isbn/9781683923664/original
illustrated Not Illustrated
dewey-hundreds 000 - Computer science, information & general works
dewey-tens 000 - Computer science, knowledge & systems
dewey-ones 005 - Computer programming, programs & data
dewey-full 005
dewey-sort 15
dewey-raw 005
dewey-search 005
doi_str_mv 10.1515/9781683923664
work_keys_str_mv AT campesatooswald tensorflowpocketprimer
status_str n
ids_txt_mv (DE-B1597)672136
carrierType_str_mv cr
is_hierarchy_title TensorFlow Pocket Primer /
_version_ 1795090205705764864
fullrecord <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02840nmm a2200589Ia 4500</leader><controlfield tag="001">9781683923664</controlfield><controlfield tag="003">DE-B1597</controlfield><controlfield tag="005">20240326120151.0</controlfield><controlfield tag="006">m|||||o||d||||||||</controlfield><controlfield tag="007">cr || ||||||||</controlfield><controlfield tag="008">240326t20192019xxu fo d z eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781683923664</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">(DE-B1597)672136</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-B1597</subfield><subfield code="b">eng</subfield><subfield code="c">DE-B1597</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxu</subfield><subfield code="c">US</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM051360</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">005</subfield><subfield code="q">OCoLC</subfield><subfield code="2">22/eng/20230216</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Campesato, Oswald, </subfield><subfield code="e">author.</subfield><subfield code="4">aut</subfield><subfield code="4">http://id.loc.gov/vocabulary/relators/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 resource (152 p.)</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">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">text file</subfield><subfield code="b">PDF</subfield><subfield code="2">rda</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Pocket Primer</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">restricted access</subfield><subfield code="u">http://purl.org/coar/access_right/c_16ec</subfield><subfield code="f">online access with authorization</subfield><subfield code="2">star</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="530" ind1=" " ind2=" "><subfield code="a">Issued also in print.</subfield></datafield><datafield tag="538" ind1=" " ind2=" "><subfield code="a">Mode of access: Internet via World Wide Web.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">In English.</subfield></datafield><datafield tag="588" ind1="0" 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="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=" "><subfield code="c">EPUB</subfield><subfield code="z">9781683923657</subfield></datafield><datafield tag="776" ind1="0" ind2=" "><subfield code="c">print</subfield><subfield code="z">9781683923640</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1515/9781683923664</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.degruyter.com/isbn/9781683923664</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="3">Cover</subfield><subfield code="u">https://www.degruyter.com/document/cover/isbn/9781683923664/original</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_BACKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_CL_CHCOMSGSEN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_DGALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EBACKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EBKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_ECL_CHCOMSGSEN</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_EEBKALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_ESTMALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">EBA_STMALL</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-deGruyter-alles</subfield></datafield></record></collection>