Unlock the Power of PyTorch 2.0 for Next-Level Natural Language Processing.
Key Features
? Comprehensive coverage of NLP concepts, techniques, and best practices.
? Hands-on examples with code implementations using PyTorch 2.0.
? Focus on real-world applications and optimizing NLP models.
? Learn to develop advanced NLP solutions with dynamic GPU acceleration.
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 will learn
? Master cutting-edge NLP techniques and integrate PyTorch 2.0 effectively.
? Implement NLP concepts with clear, hands-on examples using PyTorch 2.0.
? Tackle a wide range of NLP tasks, suitable for all experience levels.
? Explore tasks like sentiment analysis, text classification, and translation.
? Leverage advanced deep learning techniques for powerful NLP solutions.
? Preprocess text, design models, train, and evaluate their performance.
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
About the Authors
Dr. Deepti Chopra is an accomplished academician at the School of Engineering & Technology, Vivekananda Institute of Professional Studies, India, specializing in Information Technology with a primary focus on Natural Language Processing (NLP) and Artificial Intelligence (AI). With over 11 years of experience in academia, she has made significant contributions to both research and teaching. Dr. Chopra's expertise includes Machine Translation, Named Entity Recognition, Morphological Analysis, and Machine Transliteration.
Deepti began her academic journey by obtaining a Bachelor's degree in Computer Science and Engineering from Rajasthan College of Engineering for Women. Throughout her undergraduate studies, she consistently excelled and secured top positions in her college. Driven by her passion for language and technology, she pursued a Master's degree in Computer Science and Engineering from Banasthali Vidyapith, where she once again showcased exceptional skills and graduated with top honors.
