
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)
- English
- ePUB (mobile friendly)
- Available on iOS & Android
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)
About this book
Unlock the Power of PyTorch 2.0 for Next-Level Natural Language Processing.
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!
Table of Contents
1. Introduction to Natural Language Processing2. Getting Started with PyTorch3. Text Preprocessing4. Building NLP Models with PyTorch5. Advanced NLP Techniques with PyTorch6. Model Training and Evaluation7. Improving NLP Models with PyTorch8. Deployment and Productionization9. Case Studies and Practical Examples10. Future Trends in Natural Language Processing and PyTorch
Index
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Information
Table of contents
- Cover Page
- Title Page
- Copyright Page
- Dedication Page
- About the Author
- About the Technical Reviewer
- Acknowledgements
- Preface
- Errata
- 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