Transformers for natural language processing: build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3
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, q...
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Birmingham, United Kingdom]
Packt Publishing
[2022]
|
| Ausgabe: | Second edition. |
| Schriftenreihe: | Expert insight
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781803247335/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Includes index |
| Umfang: | 1 Online-Ressource (564 Seiten) illustrations |
| ISBN: | 9781803247335 |
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| spelling | Rothman, Denis VerfasserIn aut Transformers for natural language processing build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 Denis Rothman ; foreword by Antonio Gulli Second edition. [Birmingham, United Kingdom] Packt Publishing [2022] 1 Online-Ressource (564 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Expert insight Includes index 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. Artificial intelligence Data processing Artificial intelligence Computer programs Python (Computer program language) Cloud computing Intelligence artificielle ; Informatique Intelligence artificielle ; Logiciels Python (Langage de programmation) Infonuagique Artificial intelligence ; Computer programs Artificial intelligence ; Data processing Gulli, Antonio MitwirkendeR ctb |
| spellingShingle | Rothman, Denis Transformers for natural language processing build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 Artificial intelligence Data processing Artificial intelligence Computer programs Python (Computer program language) Cloud computing Intelligence artificielle ; Informatique Intelligence artificielle ; Logiciels Python (Langage de programmation) Infonuagique Artificial intelligence ; Computer programs Artificial intelligence ; Data processing |
| title | Transformers for natural language processing build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 |
| title_auth | Transformers for natural language processing build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 |
| title_exact_search | Transformers for natural language processing build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 |
| title_full | Transformers for natural language processing build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 Denis Rothman ; foreword by Antonio Gulli |
| title_fullStr | Transformers for natural language processing build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 Denis Rothman ; foreword by Antonio Gulli |
| title_full_unstemmed | Transformers for natural language processing build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 Denis Rothman ; foreword by Antonio Gulli |
| title_short | Transformers for natural language processing |
| title_sort | transformers for natural language processing build train and fine tuning deep neural network architectures for nlp with python pytorch tensorflow bert and gpt 3 |
| title_sub | build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 |
| topic | Artificial intelligence Data processing Artificial intelligence Computer programs Python (Computer program language) Cloud computing Intelligence artificielle ; Informatique Intelligence artificielle ; Logiciels Python (Langage de programmation) Infonuagique Artificial intelligence ; Computer programs Artificial intelligence ; Data processing |
| topic_facet | Artificial intelligence Data processing Artificial intelligence Computer programs Python (Computer program language) Cloud computing Intelligence artificielle ; Informatique Intelligence artificielle ; Logiciels Python (Langage de programmation) Infonuagique Artificial intelligence ; Computer programs Artificial intelligence ; Data processing |
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