Documents disponibles écrits par cet auteur

Transformers for Natural Language Processing / Rothman, Denis

Public ISBD Titre : Transformers for Natural Language Processing : build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more Type de document : texte imprimé Auteurs : Rothman, Denis, Auteur Mention d'édition : First published Editeur : Packt Publishing Année de publication : 2001 Importance : xvi, 360 Présentation : ill Format : 19 x 23.5 Cm ISBN/ISSN/EAN : 978-1-8005-6579-1 Prix : 93,50 € Note générale : Index Langues : Anglais (eng) Mots-clés : Language Processing Index. décimale : 006.1 ROT Résumé : Book DescriptionThe transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. Note de contenu : Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models Applying Transformers to Legal and Financial Documents for AI Text Summarization Matching Tokenizers and Datasets Semantic Role Labeling with BERT-Based Transformers Let Your Data Do the Talking: Story, Questions, and Answers Detecting Customer Emotions to Make Predictions Analyzing Fake News with Transformers Appendix: Answers to the Questions Transformers for Natural Language Processing : build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more [texte imprimé] / Rothman, Denis, Auteur  . -  First published . - [S.l.] : Packt Publishing, 2001 . - xvi, 360 : ill ; 19 x 23.5 Cm. ISBN : 978-1-8005-6579-1 : 93,50 € Index Langues : Anglais (eng) Mots-clés : Language Processing Index. décimale : 006.1 ROT Résumé : Book DescriptionThe transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. Note de contenu : Getting Started with the Model Architecture of the Transformer Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer Text Generation with OpenAI GPT-2 and GPT-3 Models Applying Transformers to Legal and Financial Documents for AI Text Summarization Matching Tokenizers and Datasets Semantic Role Labeling with BERT-Based Transformers Let Your Data Do the Talking: Story, Questions, and Answers Detecting Customer Emotions to Make Predictions Analyzing Fake News with Transformers Appendix: Answers to the Questions

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
FSEI2022/00024-2	006.1 ROT	Ouvrage	Bibliothèque de la Faculté des Sciences Exactes et Informatique	Informatique	Disponible
FSEI2022/00024-1	006.1 ROT	Ouvrage	Bibliothèque de la Faculté des Sciences Exactes et Informatique	Informatique	Exclu du prêt