Curriculum

13 Chapters · 13 Lessons

  1. 1

    Book Preview

    1. Applied Natural Language Processing with PyTorch 2.0 Free preview
  2. 2

    Introduction

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Introduction to Natural Language Processing

    1. (Included in full purchase)

      Introduction to Natural Language Processing

  4. 4

    Chapter 2 : Getting Started with PyTorch

    1. (Included in full purchase)

      Getting Started with PyTorch

  5. 5

    Chapter 3 : Text Preprocessing

    1. (Included in full purchase)

      Text Preprocessing

  6. 6

    Chapter 4 : Building NLP Models with PyTorch

    1. (Included in full purchase)

      Building NLP Models with PyTorch

  7. 7

    Chapter 5 : Advanced NLP Techniques with PyTorch

    1. (Included in full purchase)

      Advanced NLP Techniques with PyTorch

  8. 8

    Chapter 6 : Model Training and Evaluation

    1. (Included in full purchase)

      Model Training and Evaluation

  9. 9

    Chapter 7 : Improving NLP Models with PyTorch

    1. (Included in full purchase)

      Improving NLP Models with PyTorch

  10. 10

    Chapter 8 : Deployment and Productionization

    1. (Included in full purchase)

      Deployment and Productionization

  11. 11

    Chapter 9 : Case Studies and Practical Examples

    1. (Included in full purchase)

      Case Studies and Practical Examples

  12. 12

    Chapter 10 : Future Trends in Natural Language Processing and PyTorch

    1. (Included in full purchase)

      Future Trends in Natural Language Processing and PyTorch

  13. 13

    INDEX

    1. (Included in full purchase)

      INDEX

About the course

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!

About the Author

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.