Verfügbarkeit: The principles of deep learning theory

The principles of deep learning theory: an effective theory approach to understanding neural networks

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Roberts, Daniel A. 1987- (VerfasserIn), Yaida, Sho (VerfasserIn)
Weitere Verfasser:	Hanin, Boris ca. 20./21. Jh (MitwirkendeR)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Cambridge Cambridge University Press 2022
Schlagworte:	Deep learning (Machine learning) Deep learning
Online-Zugang:	BSB01 BTU01 FHN01 TUM01 UBM01 UER01 Volltext
Beschreibung:	1 Online-Ressource (x, 460 Seiten) Diagramme
ISBN:	9781009023405
DOI:	10.1017/9781009023405

Internformat

MARC


LEADER	00000nmm a2200000zc 4500
001	BV048311248
003	DE-604
005	20230903
007	cr\|uuu---uuuuu
008	220704s2022 \|\|\|\| o\|\|u\| \|\|\|\|\|\|eng d
020			\|a 9781009023405 \|c Online \|9 978-1-00-902340-5
024	7		\|a 10.1017/9781009023405 \|2 doi
035			\|a (ZDB-20-CBO)CR9781009023405
035			\|a (OCoLC)1335400548
035			\|a (DE-599)BVBBV048311248
040			\|a DE-604 \|b ger \|e rda
041	0		\|a eng
049			\|a DE-12 \|a DE-92 \|a DE-91 \|a DE-29 \|a DE-19
082	0		\|a 006.3/1
100	1		\|a Roberts, Daniel A. \|d 1987- \|e Verfasser \|0 (DE-588)1261776224 \|4 aut
245	1	0	\|a The principles of deep learning theory \|b an effective theory approach to understanding neural networks \|c Daniel A. Roberts mit Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University)
264		1	\|a Cambridge \|b Cambridge University Press \|c 2022
300			\|a 1 Online-Ressource (x, 460 Seiten) \|b Diagramme
336			\|b txt \|2 rdacontent
337			\|b c \|2 rdamedia
338			\|b cr \|2 rdacarrier
650		4	\|a Deep learning (Machine learning)
650	0	7	\|a Deep learning \|0 (DE-588)1135597375 \|2 gnd \|9 rswk-swf
689	0	0	\|a Deep learning \|0 (DE-588)1135597375 \|D s
689	0		\|5 DE-604
700	1		\|a Yaida, Sho \|e Verfasser \|0 (DE-588)1261764668 \|4 aut
700	1		\|a Hanin, Boris \|d ca. 20./21. Jh. \|0 (DE-588)1261776534 \|4 ctb
776	0	8	\|i Erscheint auch als \|n Druck-Ausgabe, Hardcover \|z 978-1-31-651933-2
856	4	0	\|u https://doi.org/10.1017/9781009023405 \|x Verlag \|z URL des Erstveröffentlichers \|3 Volltext
912			\|a ZDB-20-CBO
999			\|a oai:aleph.bib-bvb.de:BVB01-033690822
966	e		\|u https://doi.org/10.1017/9781009023405 \|l BSB01 \|p ZDB-20-CBO \|q BSB_PDA_CBO \|x Verlag \|3 Volltext
966	e		\|u https://doi.org/10.1017/9781009023405 \|l BTU01 \|p ZDB-20-CBO \|q BTU_PDA_CBO \|x Verlag \|3 Volltext
966	e		\|u https://doi.org/10.1017/9781009023405 \|l FHN01 \|p ZDB-20-CBO \|q FHN_PDA_CBO \|x Verlag \|3 Volltext
966	e		\|u https://doi.org/10.1017/9781009023405 \|l TUM01 \|p ZDB-20-CBO \|q TUM_Paketkauf_2022 \|x Verlag \|3 Volltext
966	e		\|u https://doi.org/10.1017/9781009023405 \|l UBM01 \|p ZDB-20-CBO \|q UBM_PDA_CBO_Kauf_2023 \|x Verlag \|3 Volltext
966	e		\|u https://doi.org/10.1017/9781009023405 \|l UER01 \|p ZDB-20-CBO \|q UER_PDA_CBO_Kauf_2023 \|x Verlag \|3 Volltext

Datensatz im Suchindex

_version_	1804184161818247168
adam_txt
any_adam_object
any_adam_object_boolean
author	Roberts, Daniel A. 1987- Yaida, Sho
author2	Hanin, Boris ca. 20./21. Jh
author2_role	ctb
author2_variant	b h bh
author_GND	(DE-588)1261776224 (DE-588)1261764668 (DE-588)1261776534
author_facet	Roberts, Daniel A. 1987- Yaida, Sho Hanin, Boris ca. 20./21. Jh
author_role	aut aut
author_sort	Roberts, Daniel A. 1987-
author_variant	d a r da dar s y sy
building	Verbundindex
bvnumber	BV048311248
collection	ZDB-20-CBO
ctrlnum	(ZDB-20-CBO)CR9781009023405 (OCoLC)1335400548 (DE-599)BVBBV048311248
dewey-full	006.3/1
dewey-hundreds	000 - Computer science, information, general works
dewey-ones	006 - Special computer methods
dewey-raw	006.3/1
dewey-search	006.3/1
dewey-sort	16.3 11
dewey-tens	000 - Computer science, information, general works
discipline	Informatik
discipline_str_mv	Informatik
doi_str_mv	10.1017/9781009023405
format	Electronic eBook
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02293nmm a2200469zc 4500</leader><controlfield tag="001">BV048311248</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230903 </controlfield><controlfield tag="007">cr\|uuu---uuuuu</controlfield><controlfield tag="008">220704s2022 \|\|\|\| o\|\|u\| \|\|\|\|\|\|eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781009023405</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-00-902340-5</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/9781009023405</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-20-CBO)CR9781009023405</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1335400548</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048311248</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-12</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-19</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/1</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Roberts, Daniel A.</subfield><subfield code="d">1987-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1261776224</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The principles of deep learning theory</subfield><subfield code="b">an effective theory approach to understanding neural networks</subfield><subfield code="c">Daniel A. Roberts mit Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (x, 460 Seiten)</subfield><subfield code="b">Diagramme</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="650" ind1=" " ind2="4"><subfield code="a">Deep learning (Machine learning)</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yaida, Sho</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1261764668</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hanin, Boris</subfield><subfield code="d">ca. 20./21. Jh.</subfield><subfield code="0">(DE-588)1261776534</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Hardcover</subfield><subfield code="z">978-1-31-651933-2</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/9781009023405</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-20-CBO</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033690822</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009023405</subfield><subfield code="l">BSB01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BSB_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009023405</subfield><subfield code="l">BTU01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BTU_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009023405</subfield><subfield code="l">FHN01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">FHN_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009023405</subfield><subfield code="l">TUM01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">TUM_Paketkauf_2022</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009023405</subfield><subfield code="l">UBM01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">UBM_PDA_CBO_Kauf_2023</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009023405</subfield><subfield code="l">UER01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">UER_PDA_CBO_Kauf_2023</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection>
id	DE-604.BV048311248
illustrated	Not Illustrated
index_date	2024-07-03T20:09:36Z
indexdate	2024-07-10T09:34:57Z
institution	BVB
isbn	9781009023405
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-033690822
oclc_num	1335400548
open_access_boolean
owner	DE-12 DE-92 DE-91 DE-BY-TUM DE-29 DE-19 DE-BY-UBM
owner_facet	DE-12 DE-92 DE-91 DE-BY-TUM DE-29 DE-19 DE-BY-UBM
physical	1 Online-Ressource (x, 460 Seiten) Diagramme
psigel	ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO BTU_PDA_CBO ZDB-20-CBO FHN_PDA_CBO ZDB-20-CBO TUM_Paketkauf_2022 ZDB-20-CBO UBM_PDA_CBO_Kauf_2023 ZDB-20-CBO UER_PDA_CBO_Kauf_2023
publishDate	2022
publishDateSearch	2022
publishDateSort	2022
publisher	Cambridge University Press
record_format	marc
spelling	Roberts, Daniel A. 1987- Verfasser (DE-588)1261776224 aut The principles of deep learning theory an effective theory approach to understanding neural networks Daniel A. Roberts mit Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University) Cambridge Cambridge University Press 2022 1 Online-Ressource (x, 460 Seiten) Diagramme txt rdacontent c rdamedia cr rdacarrier Deep learning (Machine learning) Deep learning (DE-588)1135597375 gnd rswk-swf Deep learning (DE-588)1135597375 s DE-604 Yaida, Sho Verfasser (DE-588)1261764668 aut Hanin, Boris ca. 20./21. Jh. (DE-588)1261776534 ctb Erscheint auch als Druck-Ausgabe, Hardcover 978-1-31-651933-2 https://doi.org/10.1017/9781009023405 Verlag URL des Erstveröffentlichers Volltext
spellingShingle	Roberts, Daniel A. 1987- Yaida, Sho The principles of deep learning theory an effective theory approach to understanding neural networks Deep learning (Machine learning) Deep learning (DE-588)1135597375 gnd
subject_GND	(DE-588)1135597375
title	The principles of deep learning theory an effective theory approach to understanding neural networks
title_auth	The principles of deep learning theory an effective theory approach to understanding neural networks
title_exact_search	The principles of deep learning theory an effective theory approach to understanding neural networks
title_exact_search_txtP	The principles of deep learning theory an effective theory approach to understanding neural networks
title_full	The principles of deep learning theory an effective theory approach to understanding neural networks Daniel A. Roberts mit Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University)
title_fullStr	The principles of deep learning theory an effective theory approach to understanding neural networks Daniel A. Roberts mit Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University)
title_full_unstemmed	The principles of deep learning theory an effective theory approach to understanding neural networks Daniel A. Roberts mit Sho Yaida (Meta AI) ; based on research in collaboration with Boris Hanin (Princeton University)
title_short	The principles of deep learning theory
title_sort	the principles of deep learning theory an effective theory approach to understanding neural networks
title_sub	an effective theory approach to understanding neural networks
topic	Deep learning (Machine learning) Deep learning (DE-588)1135597375 gnd
topic_facet	Deep learning (Machine learning) Deep learning
url	https://doi.org/10.1017/9781009023405
work_keys_str_mv	AT robertsdaniela theprinciplesofdeeplearningtheoryaneffectivetheoryapproachtounderstandingneuralnetworks AT yaidasho theprinciplesofdeeplearningtheoryaneffectivetheoryapproachtounderstandingneuralnetworks AT haninboris theprinciplesofdeeplearningtheoryaneffectivetheoryapproachtounderstandingneuralnetworks

Verfügbarkeit

Es ist kein Print-Exemplar vorhanden.

Fernleihe Bestellen Achtung: Nicht im THWS-Bestand! Volltext öffnen

MARC

Datensatz im Suchindex

Es ist kein Print-Exemplar vorhanden.

Ähnliche Einträge