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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 DescriptionNatural 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 Contents1. 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 PyTorchIndex
About the AuthorsDr. 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.
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.
Motivated to delve deeper into her research interests, Deepti pursued a Ph.D. in Computer Science and Engineering from Banasthali Vidyapith. Her doctoral research revolved around enhancing the quality of Machine Translation, and she achieved remarkable success in this area. Consequently, she earned a Ph.D. degree with a specialization in Quality Improvement of Machine Translation. Her doctoral work resulted in the publication of numerous research papers and the granting of an Australian patent for her innovative approach to Named Entity Translation.
Deepti's commitment to advancing knowledge in her field is reflected in her extensive publication record. She has authored multiple books such as Building Machine Learning Systems using Python and Flutter and Dart: Up and Running. Additionally, her research papers have been published in reputable international conferences and journals.
Deepti's commitment and her ability to translate research findings into practical solutions solidify her position as a prominent figure in the field.
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