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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Birmingham, United Kingdom] :
Packt Publishing,
[2022]
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| Edición: | Second edition. |
| Colección: | Expert insight.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Rothman, Denis, |e author. | |
| 245 | 1 | 0 | |a Transformers for natural language processing : |b build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 / |c Denis Rothman ; foreword by Antonio Gulli. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a [Birmingham, United Kingdom] : |b Packt Publishing, |c [2022] | |
| 300 | |a 1 online resource (564 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 500 | |a Includes index. | ||
| 520 | |a 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Artificial intelligence |x Data processing. | |
| 650 | 0 | |a Artificial intelligence |x Computer programs. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Cloud computing. | |
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| 650 | 6 | |a Intelligence artificielle |x Logiciels. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Infonuagique. | |
| 650 | 7 | |a Artificial intelligence |x Computer programs |2 fast | |
| 650 | 7 | |a Artificial intelligence |x Data processing |2 fast | |
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| 650 | 7 | |a Python (Computer program language) |2 fast | |
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| 830 | 0 | |a Expert insight. | |
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