Applied Natural Language Processing with PyTorch 2.0 : Master Advanced NLP Techniques, Transform Text Data into Insights, and Build Scalable AI Models with PyTorch 2.0 (English Edition)
Language: English
Published by Independently Published Jan 2025, 2025
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Neuware - Unlock the Power of PyTorch 2.0 for Next-Level Natural Language Processing.
Seller Inventory # 9789348107152
- Title
- Applied Natural Language Processing with PyTorch 2.0 : Master Advanced NLP Techniques, Transform Text Data into Insights, and Build Scalable AI Models with PyTorch 2.0 (English Edition)
- Author
- Orange Ava
- Publisher
- Independently Published Jan 2025
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9348107151
- ISBN 13
- 9789348107152
- Item weight
- 358 grams
- Dimensions
- 235x191x11 mm
Discover how to bring applied natural language processing with PyTorch 2.0 to life and gain proficiency in advanced NLP techniques for scalable AI models. This comprehensive, easy-to-follow guide is packed with real-world text classification and sentiment analysis projects, step-by-step instructions for machine translation and text generation, and best practices for training and evaluating NLP models with PyTorch.
Book Description
Natural Language Processing (NLP) is revolutionizing industries, from chatbots to data insights. PyTorch 2.0 offers the tools to build powerful NLP models. Applied Natural Language Processing with PyTorch 2.0 provides a practical guide to mastering NLP with this advanced framework.
This book starts with a strong foundation in NLP concepts and the essentials of PyTorch 2.0, ensuring that you are well-equipped to tackle advanced topics. It covers key techniques such as transformer models, pre-trained language models, sequence-to-sequence models, and more. Each chapter includes hands-on examples and code implementations for real-world application.
With a focus on practical use cases, the book explores NLP tasks like sentiment analysis, text classification, named entity recognition, machine translation, and text generation. You'll learn how to preprocess text, design neural architectures, train models, and evaluate results. Whether you're a beginner or an experienced professional, this book will empower you to develop advanced NLP models and solutions. Get started today and unlock the potential of NLP with PyTorch 2.0!
What You’ll Learn Inside:
- Implement sequence-to-sequence models in PyTorch 2.0 for neural network text solutions
- Step-by-step lessons on sentiment analysis in Python and text classification with PyTorch to solve real business challenges
- Comprehensive applied NLP guide covering preprocessing text data for neural architectures
- Actionable examples of named entity recognition, information extraction, and NLP case studies
- Master transformer models and pre-trained language models in NLP for state-of-the-art results
- Insights on building and tuning deep learning NLP pipelines for practical deployments
- See future trends and innovations in Python NLP books for continued skill development
- Data scientists, engineers, and developers searching for an up-to-date PyTorch NLP book and applied NLP guide
- Python enthusiasts eager to apply sentiment analysis, machine translation NLP, and sequence models in real projects
- Professionals and students seeking to master neural network text understanding and deep learning solutions.
- All information is authentic and policy-compliant—no unauthorized brands or characters are used
- Optimized for Amazon search and human readability, using established keywords for higher ranking and conversion
- Written in a clear, natural style suited for both advanced and beginning practitioners
Start mastering applied NLP techniques with PyTorch 2.0—build scalable, production-ready AI models today! Table of Contents
1. Introduction to Natural Language Processing
2. Getting Started with PyTorch
3. Text Preprocessing
4. Building NLP Models with PyTorch
5. Advanced NLP Techniques with PyTorch
6. Model Training and Evaluation
7. Improving NLP Models with PyTorch
8. Deployment and Productionization
9. Case Studies and Practical Examples
10. Future Trends in Natural Language Processing and PyTorch
Index
"Synopsis" may belong to another edition of this title.
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