Transformers for natural language processing: build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
2021
|
| Schriftenreihe: | Expert insight
|
| Schlagwörter: | |
| Links: | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2739556 https://ebookcentral.proquest.com/lib/uniregensburg-ebooks/detail.action?docID=6467893 http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2739556 https://ebookcentral.proquest.com/lib/erlangen/detail.action?docID=6467893 |
| Abstract: | Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and AllenNLP. |
| Umfang: | 1 Online-Ressource (xvi, 360 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781800568631 |
Internformat
MARC
| LEADER | 00000nam a22000001c 4500 | ||
|---|---|---|---|
| 001 | BV047160289 | ||
| 003 | DE-604 | ||
| 005 | 20230118 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 210224s2021 xx a||| o|||| 00||| eng d | ||
| 020 | |a 9781800568631 |c Online |9 978-1-80056-863-1 | ||
| 035 | |a (ZDB-4-NLEBK)2739556 | ||
| 035 | |a (ZDB-30-PQE)EBC6467893 | ||
| 035 | |a (OCoLC)1240400328 | ||
| 035 | |a (DE-599)KEP061509248 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-355 |a DE-706 |a DE-92 |a DE-29 | ||
| 084 | |a ST 306 |0 (DE-625)143654: |2 rvk | ||
| 100 | 1 | |a Rothman, Denis |e Verfasser |0 (DE-588)1221752987 |4 aut | |
| 245 | 1 | 0 | |a Transformers for natural language processing |b build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more |c Denis Rothman |
| 264 | 1 | |a Birmingham ; Mumbai |b Packt |c 2021 | |
| 300 | |a 1 Online-Ressource (xvi, 360 Seiten) |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Expert insight | |
| 520 | 3 | |a Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and AllenNLP. | |
| 650 | 0 | 7 | |a Deep Learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Natürliche Sprache |0 (DE-588)4041354-8 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Electronic books | |
| 689 | 0 | 0 | |a Natürliche Sprache |0 (DE-588)4041354-8 |D s |
| 689 | 0 | 1 | |a Deep Learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-1-80056-579-1 |
| 912 | |a ZDB-30-PQE | ||
| 912 | |a ZDB-4-NLEBK | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-032565928 | |
| 966 | e | |u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2739556 |l DE-92 |p ZDB-4-NLEBK |x Aggregator |3 Volltext | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/uniregensburg-ebooks/detail.action?docID=6467893 |l DE-355 |p ZDB-30-PQE |q UBR_Einzelkauf 2021 |x Aggregator |3 Volltext | |
| 966 | e | |u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2739556 |l DE-706 |p ZDB-4-NLEBK |q UBY01_DDA21 |x Aggregator |3 Volltext | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/erlangen/detail.action?docID=6467893 |l DE-29 |p ZDB-30-PQE |q UER_PDA_PQE_Kauf_2023 |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1823932147889602561 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Rothman, Denis |
| author_GND | (DE-588)1221752987 |
| author_facet | Rothman, Denis |
| author_role | aut |
| author_sort | Rothman, Denis |
| author_variant | d r dr |
| building | Verbundindex |
| bvnumber | BV047160289 |
| classification_rvk | ST 306 |
| collection | ZDB-30-PQE ZDB-4-NLEBK |
| ctrlnum | (ZDB-4-NLEBK)2739556 (ZDB-30-PQE)EBC6467893 (OCoLC)1240400328 (DE-599)KEP061509248 |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a22000001c 4500</leader><controlfield tag="001">BV047160289</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230118</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">210224s2021 xx a||| o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781800568631</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-80056-863-1</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-4-NLEBK)2739556</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC6467893</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1240400328</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP061509248</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-355</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-29</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 306</subfield><subfield code="0">(DE-625)143654:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rothman, Denis</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1221752987</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Transformers for natural language processing</subfield><subfield code="b">build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more</subfield><subfield code="c">Denis Rothman</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham ; Mumbai</subfield><subfield code="b">Packt</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xvi, 360 Seiten)</subfield><subfield code="b">Illustrationen, 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="490" ind1="0" ind2=" "><subfield code="a">Expert insight</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and AllenNLP.</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="650" ind1="0" ind2="7"><subfield code="a">Natürliche Sprache</subfield><subfield code="0">(DE-588)4041354-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic books</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Natürliche Sprache</subfield><subfield code="0">(DE-588)4041354-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><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="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-1-80056-579-1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-NLEBK</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032565928</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2739556</subfield><subfield code="l">DE-92</subfield><subfield code="p">ZDB-4-NLEBK</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/uniregensburg-ebooks/detail.action?docID=6467893</subfield><subfield code="l">DE-355</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">UBR_Einzelkauf 2021</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2739556</subfield><subfield code="l">DE-706</subfield><subfield code="p">ZDB-4-NLEBK</subfield><subfield code="q">UBY01_DDA21</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/erlangen/detail.action?docID=6467893</subfield><subfield code="l">DE-29</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">UER_PDA_PQE_Kauf_2023</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV047160289 |
| illustrated | Illustrated |
| indexdate | 2025-02-13T09:00:43Z |
| institution | BVB |
| isbn | 9781800568631 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032565928 |
| oclc_num | 1240400328 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-706 DE-92 DE-29 |
| owner_facet | DE-355 DE-BY-UBR DE-706 DE-92 DE-29 |
| physical | 1 Online-Ressource (xvi, 360 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-30-PQE ZDB-4-NLEBK ZDB-30-PQE UBR_Einzelkauf 2021 ZDB-4-NLEBK UBY01_DDA21 ZDB-30-PQE UER_PDA_PQE_Kauf_2023 |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Packt |
| record_format | marc |
| series2 | Expert insight |
| spelling | Rothman, Denis Verfasser (DE-588)1221752987 aut Transformers for natural language processing build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more Denis Rothman Birmingham ; Mumbai Packt 2021 1 Online-Ressource (xvi, 360 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Expert insight Being the first book in the market to dive deep into the Transformers, it is a step-by-step guide for data and AI practitioners to help enhance the performance of language understanding and gain expertise with hands-on implementation of transformers using PyTorch, TensorFlow, Hugging Face, Trax, and AllenNLP. Deep Learning (DE-588)1135597375 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Electronic books Natürliche Sprache (DE-588)4041354-8 s Deep Learning (DE-588)1135597375 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-80056-579-1 |
| spellingShingle | Rothman, Denis Transformers for natural language processing build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more Deep Learning (DE-588)1135597375 gnd Natürliche Sprache (DE-588)4041354-8 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4041354-8 |
| title | Transformers for natural language processing build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more |
| title_auth | Transformers for natural language processing build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more |
| title_exact_search | Transformers for natural language processing build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more |
| title_full | Transformers for natural language processing build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more Denis Rothman |
| title_fullStr | Transformers for natural language processing build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more Denis Rothman |
| title_full_unstemmed | Transformers for natural language processing build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more Denis Rothman |
| title_short | Transformers for natural language processing |
| title_sort | transformers for natural language processing build innovative deep neural network architectures for nlp with python pytorch tensorflow bert roberta and more |
| title_sub | build innovative deep neural network architectures for NLP with Python, Pytorch, TensorFlow, BERT, RoBERTa, and more |
| topic | Deep Learning (DE-588)1135597375 gnd Natürliche Sprache (DE-588)4041354-8 gnd |
| topic_facet | Deep Learning Natürliche Sprache |
| work_keys_str_mv | AT rothmandenis transformersfornaturallanguageprocessingbuildinnovativedeepneuralnetworkarchitecturesfornlpwithpythonpytorchtensorflowbertrobertaandmore |