Transformers for natural language processing and computer vision: explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3
Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides yo...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing Ltd.
2024
|
| Ausgabe: | Third edition. |
| Schriftenreihe: | Expert insight
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781805128724/?ar |
| Zusammenfassung: | Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You'll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You'll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices. |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (730 Seiten) illustrations |
| ISBN: | 9781805128724 |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-102206406 | ||
| 003 | DE-627-1 | ||
| 005 | 20241001123306.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240404s2024 xx |||||o 00| ||eng c | ||
| 020 | |a 9781805128724 |9 978-1-80512-872-4 | ||
| 035 | |a (DE-627-1)102206406 | ||
| 035 | |a (DE-599)KEP102206406 | ||
| 035 | |a (ORHE)9781805128724 | ||
| 035 | |a (DE-627-1)102206406 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a 006.3 |2 23/eng/20240916 | |
| 100 | 1 | |a Rothman, Denis |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Transformers for natural language processing and computer vision |b explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 |c Denis Rothman |
| 250 | |a Third edition. | ||
| 264 | 1 | |a Birmingham, UK |b Packt Publishing Ltd. |c 2024 | |
| 300 | |a 1 Online-Ressource (730 Seiten) |b illustrations | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Expert insight | |
| 500 | |a Includes bibliographical references and index | ||
| 520 | |a Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You'll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You'll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices. | ||
| 630 | 2 | 0 | |a ChatGPT |
| 650 | 0 | |a Artificial intelligence |x Data processing | |
| 650 | 0 | |a Natural language processing (Computer science) | |
| 650 | 0 | |a Cloud computing | |
| 650 | 4 | |a Intelligence artificielle ; Informatique | |
| 650 | 4 | |a Traitement automatique des langues naturelles | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781805128724/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-102206406 |
|---|---|
| _version_ | 1821494934026321920 |
| adam_text | |
| any_adam_object | |
| author | Rothman, Denis |
| author_facet | Rothman, Denis |
| author_role | aut |
| author_sort | Rothman, Denis |
| author_variant | d r dr |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)102206406 (DE-599)KEP102206406 (ORHE)9781805128724 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Third edition. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02833cam a22004212 4500</leader><controlfield tag="001">ZDB-30-ORH-102206406</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20241001123306.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240404s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781805128724</subfield><subfield code="9">978-1-80512-872-4</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)102206406</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP102206406</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781805128724</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)102206406</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="082" ind1="0" ind2=" "><subfield code="a">006.3</subfield><subfield code="2">23/eng/20240916</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rothman, Denis</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Transformers for natural language processing and computer vision</subfield><subfield code="b">explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3</subfield><subfield code="c">Denis Rothman</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Third edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK</subfield><subfield code="b">Packt Publishing Ltd.</subfield><subfield code="c">2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (730 Seiten)</subfield><subfield code="b">illustrations</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="490" ind1="0" ind2=" "><subfield code="a">Expert insight</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You'll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You'll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.</subfield></datafield><datafield tag="630" ind1="2" ind2="0"><subfield code="a">ChatGPT</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield><subfield code="x">Data processing</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Natural language processing (Computer science)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Cloud computing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligence artificielle ; Informatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traitement automatique des langues naturelles</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/-/9781805128724/?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="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-102206406 |
| illustrated | Illustrated |
| indexdate | 2025-01-17T11:22:15Z |
| institution | BVB |
| isbn | 9781805128724 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (730 Seiten) illustrations |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing Ltd. |
| record_format | marc |
| series2 | Expert insight |
| spelling | Rothman, Denis VerfasserIn aut Transformers for natural language processing and computer vision explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 Denis Rothman Third edition. Birmingham, UK Packt Publishing Ltd. 2024 1 Online-Ressource (730 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Expert insight Includes bibliographical references and index Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You'll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You'll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices. ChatGPT Artificial intelligence Data processing Natural language processing (Computer science) Cloud computing Intelligence artificielle ; Informatique Traitement automatique des langues naturelles |
| spellingShingle | Rothman, Denis Transformers for natural language processing and computer vision explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 ChatGPT Artificial intelligence Data processing Natural language processing (Computer science) Cloud computing Intelligence artificielle ; Informatique Traitement automatique des langues naturelles |
| title | Transformers for natural language processing and computer vision explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 |
| title_auth | Transformers for natural language processing and computer vision explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 |
| title_exact_search | Transformers for natural language processing and computer vision explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 |
| title_full | Transformers for natural language processing and computer vision explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 Denis Rothman |
| title_fullStr | Transformers for natural language processing and computer vision explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 Denis Rothman |
| title_full_unstemmed | Transformers for natural language processing and computer vision explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 Denis Rothman |
| title_short | Transformers for natural language processing and computer vision |
| title_sort | transformers for natural language processing and computer vision explore generative ai and large language models with hugging face chatgpt gpt 4v and dall e3 |
| title_sub | explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 |
| topic | ChatGPT Artificial intelligence Data processing Natural language processing (Computer science) Cloud computing Intelligence artificielle ; Informatique Traitement automatique des langues naturelles |
| topic_facet | ChatGPT Artificial intelligence Data processing Natural language processing (Computer science) Cloud computing Intelligence artificielle ; Informatique Traitement automatique des langues naturelles |
| work_keys_str_mv | AT rothmandenis transformersfornaturallanguageprocessingandcomputervisionexploregenerativeaiandlargelanguagemodelswithhuggingfacechatgptgpt4vanddalle3 |