Transformers for Natural Language Processing (notice n° 1324239)

détails MARC
000 -LEADER
fixed length control field 03478cam a2200289zu 4500
003 - CONTROL NUMBER IDENTIFIER
control field FRCYB88929010
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250429181235.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250429s2022 fr | o|||||0|0|||eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781803247335
035 ## - SYSTEM CONTROL NUMBER
System control number FRCYB88929010
040 ## - CATALOGING SOURCE
Original cataloging agency FR-PaCSA
Language of cataloging en
Transcribing agency
Description conventions rda
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Rothman, Denis
245 01 - TITLE STATEMENT
Title Transformers for Natural Language Processing
Statement of responsibility, etc. ['Rothman, Denis', 'Gulli, Antonio']
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer Packt Publishing
Date of production, publication, distribution, manufacture, or copyright notice 2022
300 ## - PHYSICAL DESCRIPTION
Extent p.
336 ## - CONTENT TYPE
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type code c
Source rdamdedia
338 ## - CARRIER TYPE
Carrier type code c
Source rdacarrier
520 ## - SUMMARY, ETC.
Summary, etc. Under the hood working of transformers, fine-tuning GPT-3 models, DeBERTa, vision models, and the start of Metaverse, using a variety of NLP platforms: Hugging Face, OpenAI API, Trax, and AllenNLP Key Features Implement models, such as BERT, Reformer, and T5, that outperform classical language models Compare NLP applications using GPT-3, GPT-2, and other transformers Analyze advanced use cases, including polysemy, cross-lingual learning, and computer vision Book Description Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers can create code using just a brief description. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets. What you will learn Discover new ways of performing NLP techniques with the latest pretrained transformers Grasp the workings of the original Transformer, GPT-3, BERT, T5, DeBERTa, and Reformer Find out how ViT and CLIP label images (including blurry ones!) and reconstruct images using DALL-E Carry out sentiment analysis, text summarization, casual language analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3 Measure the productivity of key transformers to define their scope, potential, and limits in production Who this book is for If you want to learn about and apply transformers to your natural language (and image) data, this book is for you. A good understanding of NLP, Python, and deep learning is required to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters of this book.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Rothman, Denis
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Gulli, Antonio
856 40 - ELECTRONIC LOCATION AND ACCESS
Access method Cyberlibris
Uniform Resource Identifier <a href="https://international.scholarvox.com/netsen/book/88929010">https://international.scholarvox.com/netsen/book/88929010</a>
Electronic format type text/html
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