Transformers for natural language processing :: build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 /
BONUS OpenAI ChatGPT, GPT-4, and DALL-E notebooks in the book's GitHub repository - Start coding with these SOTA transformers.OpenAI's GPT-3 and Hugging Face transformers for language tasks in one book. Plus, get a taste of the future of transformers, including computer vision tasks and co...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
[2022]
|
| Ausgabe: | Second edition. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | BONUS OpenAI ChatGPT, GPT-4, and DALL-E notebooks in the book's GitHub repository - Start coding with these SOTA transformers.OpenAI's GPT-3 and Hugging Face transformers for language tasks in one book. Plus, get a taste of the future of transformers, including computer vision tasks and code writing and assistance with Codex and GitHub Copilot.Purchase of the print or Kindle book includes a free eBook in PDF formatKey FeaturesPretrain a BERT-based model from scratch using Hugging FaceFine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your dataPerform root cause analysis on hard NLP problemsBook DescriptionTransformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs?Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses.You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model.If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides.The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details).You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using Codex.By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective!What you will learnFind out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-EDiscover new techniques to investigate complex language problemsCompare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformersCarry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3Measure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is forIf you want to learn about and apply transformers to your natural language (and image) data, this book is for you.You'll need a good understanding of Python and deep learning and a basic understanding of NLP 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. And, don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community and author, Denis Rothman. So, he'll be there to guide you on your transformers journey! |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource. |
| ISBN: | 9781803243481 1803243481 |
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| 520 | |a BONUS OpenAI ChatGPT, GPT-4, and DALL-E notebooks in the book's GitHub repository - Start coding with these SOTA transformers.OpenAI's GPT-3 and Hugging Face transformers for language tasks in one book. Plus, get a taste of the future of transformers, including computer vision tasks and code writing and assistance with Codex and GitHub Copilot.Purchase of the print or Kindle book includes a free eBook in PDF formatKey FeaturesPretrain a BERT-based model from scratch using Hugging FaceFine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your dataPerform root cause analysis on hard NLP problemsBook DescriptionTransformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs?Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses.You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model.If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides.The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details).You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using Codex.By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective!What you will learnFind out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-EDiscover new techniques to investigate complex language problemsCompare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformersCarry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3Measure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is forIf you want to learn about and apply transformers to your natural language (and image) data, this book is for you.You'll need a good understanding of Python and deep learning and a basic understanding of NLP 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. And, don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community and author, Denis Rothman. So, he'll be there to guide you on your transformers journey! | ||
| 505 | 0 | |a Table of Contents What are Transformers? Getting Started with the Architecture of the Transformer Model Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer The Rise of Suprahuman Transformers with GPT-3 Engines 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 Interpreting Black Box Transformer Models From NLP to Task-Agnostic Transformer Models The Emergence of Transformer-Driven Copilots The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4' Appendix I -- Terminology of Transformer Models Appendix II -- Hardware Constraints for Transformer Models Appendix III -- Generic Text Completion with GPT-2 Appendix IV -- Custom Text Completion with GPT-2 Appendix V -- Answers to the Questions. | |
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| contents | Table of Contents What are Transformers? Getting Started with the Architecture of the Transformer Model Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer The Rise of Suprahuman Transformers with GPT-3 Engines 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 Interpreting Black Box Transformer Models From NLP to Task-Agnostic Transformer Models The Emergence of Transformer-Driven Copilots The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4' Appendix I -- Terminology of Transformer Models Appendix II -- Hardware Constraints for Transformer Models Appendix III -- Generic Text Completion with GPT-2 Appendix IV -- Custom Text Completion with GPT-2 Appendix V -- Answers to the Questions. |
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| spelling | Rothman, Denis, author. Transformers for natural language processing : build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 / Denis Rothman ; foreword by Antonio Gulli. Second edition. Birmingham, UK : Packt Publishing, [2022] 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes index. Online resource; title from PDF title page (EBSCO, viewed June 30, 2022). BONUS OpenAI ChatGPT, GPT-4, and DALL-E notebooks in the book's GitHub repository - Start coding with these SOTA transformers.OpenAI's GPT-3 and Hugging Face transformers for language tasks in one book. Plus, get a taste of the future of transformers, including computer vision tasks and code writing and assistance with Codex and GitHub Copilot.Purchase of the print or Kindle book includes a free eBook in PDF formatKey FeaturesPretrain a BERT-based model from scratch using Hugging FaceFine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your dataPerform root cause analysis on hard NLP problemsBook DescriptionTransformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs?Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses.You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model.If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides.The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details).You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using Codex.By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective!What you will learnFind out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-EDiscover new techniques to investigate complex language problemsCompare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformersCarry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3Measure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is forIf you want to learn about and apply transformers to your natural language (and image) data, this book is for you.You'll need a good understanding of Python and deep learning and a basic understanding of NLP 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. And, don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community and author, Denis Rothman. So, he'll be there to guide you on your transformers journey! Table of Contents What are Transformers? Getting Started with the Architecture of the Transformer Model Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer The Rise of Suprahuman Transformers with GPT-3 Engines 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 Interpreting Black Box Transformer Models From NLP to Task-Agnostic Transformer Models The Emergence of Transformer-Driven Copilots The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4' Appendix I -- Terminology of Transformer Models Appendix II -- Hardware Constraints for Transformer Models Appendix III -- Generic Text Completion with GPT-2 Appendix IV -- Custom Text Completion with GPT-2 Appendix V -- Answers to the Questions. Artificial intelligence Data processing. http://id.loc.gov/authorities/subjects/sh85008182 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Intelligence artificielle Informatique. Python (Langage de programmation) Infonuagique. Artificial intelligence Data processing fast Cloud computing fast Python (Computer program language) fast Gulli, Antonio, writer of foreword. http://id.loc.gov/authorities/names/no2018098425 has work: Transformers for natural language processing (Text) https://id.oclc.org/worldcat/entity/E39PCGCmQfdMGRpQv7MFpdmfdP https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3197830 Volltext |
| spellingShingle | Rothman, Denis Transformers for natural language processing : build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 / Table of Contents What are Transformers? Getting Started with the Architecture of the Transformer Model Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer The Rise of Suprahuman Transformers with GPT-3 Engines 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 Interpreting Black Box Transformer Models From NLP to Task-Agnostic Transformer Models The Emergence of Transformer-Driven Copilots The Consolidation of Suprahuman Transformers with OpenAI's ChatGPT and GPT-4' Appendix I -- Terminology of Transformer Models Appendix II -- Hardware Constraints for Transformer Models Appendix III -- Generic Text Completion with GPT-2 Appendix IV -- Custom Text Completion with GPT-2 Appendix V -- Answers to the Questions. Artificial intelligence Data processing. http://id.loc.gov/authorities/subjects/sh85008182 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Intelligence artificielle Informatique. Python (Langage de programmation) Infonuagique. Artificial intelligence Data processing fast Cloud computing fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008182 http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh2008004883 |
| title | Transformers for natural language processing : build, train, and fine-tune 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-tune 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-tune 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-tune 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-tune 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-tune 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 tune deep neural network architectures for nlp with python pytorch tensorflow bert and gpt 3 |
| title_sub | build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 / |
| topic | Artificial intelligence Data processing. http://id.loc.gov/authorities/subjects/sh85008182 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Intelligence artificielle Informatique. Python (Langage de programmation) Infonuagique. Artificial intelligence Data processing fast Cloud computing fast Python (Computer program language) fast |
| topic_facet | Artificial intelligence Data processing. Python (Computer program language) Cloud computing. Intelligence artificielle Informatique. Python (Langage de programmation) Infonuagique. Artificial intelligence Data processing Cloud computing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3197830 |
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