Applied Natural Language Processing with PyTorch 2.0 9789348107527, 9789348107152
Unlock the Power of PyTorch 2.0 for Next-Level Natural Language Processing. Book Description Natural Language Processin
270 130 8MB
English Pages 204 Year 2025
- Author / Uploaded
- Dr. Deepti Chopra
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
Introduction
Structure
Understanding NLP
Linguistic Foundations
Computational Challenges
NLP Techniques and Approaches
Applications of NLP
NLP Challenges and Approaches
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
2. Getting Started with PyTorch
Introduction
Structure
Introduction to PyTorch
The Significance of PyTorch
Key Highlights
Installing PyTorch 2.0
Prerequisites
Installing PyTorch via pip
CPU Version Installation
Installing PyTorch
PyTorch Basics
Tensors and Operations
Tensors: The Building Blocks
Creating Tensors
Tensor Operations
Autograd: Automatic Differentiation
Neural Networks with PyTorch
Training a Neural Network
GPU Acceleration with PyTorch
The Need to Use GPUs
Checking for GPU Availability
Moving Tensors to GPU
GPU Accelerated Training
GPU Considerations
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
3. Text Preprocessing
Introduction
Structure
Tokenization
The Tokenization Process
Need for Tokenization
Challenges in Tokenization
Stop Word Removal
Need for Stop Word Removal
Challenges in Stop Word Removal
Implementing Stop Word Removal
Stemming and Lemmatization
Stemming
Lemmatization
Challenges in Stemming and Lemmatization
Implementing Stemming and Lemmatization
Handling Special Characters and Punctuation
The Role of Special Characters and Punctuation
Methods for Handling Special Characters and Punctuation
Challenges and Considerations
Word Embeddings and Word2Vec
Word Embeddings: The Essence of Vector Representations
Word2Vec: A Breakthrough in Word Embeddings
Importance of Word2Vec
Challenges and Considerations
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
4. Building NLP Models with PyTorch
Introduction
Structure
Text Classification
Sentiment Analysis
Named Entity Recognition (NER)
Understanding Named Entity Recognition
Part-of-Speech (POS) Tagging
Machine Translation
Understanding Machine Translation
Text Generation with RNNs
Recurrent Neural Networks for Text Generation
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
5. Advanced NLP Techniques with PyTorch
Introduction
Structure
Sequence-to-Sequence Models
Attention Mechanisms
Transformer Models
Transfer Learning For NLP
Language Modeling with GPT-3.5
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
6. Model Training and Evaluation
Introduction
Structure
Dataset Preparation
Training Pipelines
Model Evaluation Metrics
Hyperparameter Tuning
Overfitting and Regularization Techniques
Practical Tips to Combat Overfitting
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
7. Improving NLP Models with PyTorch
Introduction
Structure
Handling Out-of-Vocabulary (OOV) Words
Handling Long Sequences
Batch Processing and Data Loaders
Data Loaders in PyTorch
Implementing Effective Data Loaders
Advanced Optimization Techniques
Advanced Optimization Algorithms
Implementing Optimization Techniques in PyTorch
Model Interpretability and Explainability
Importance of Interpretability and Explainability
Techniques for Improving Interpretability and Explainability
Working of Attention Mechanism
Local Interpretable Model-agnostic Explanations (LIME)
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
8. Deployment and Productionization
Introduction
Structure
Exporting PyTorch Models
Deployment Strategies (Server, Edge, Cloud)
Server-Based Deployments
Scaling and Performance Optimization
Monitoring and Debugging
Ethical Considerations in NLP
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
9. Case Studies and Practical Examples
Introduction
Structure
Sentiment Analysis on Social Media Data
Challenges in Sentiment Analysis
Text Classification For News Articles
Chatbot Development with PyTorch
Neural Machine Translation System
Question Answering System
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
10. Future Trends in Natural Language Processing and PyTorch
Introduction
Structure
Advances in Pre-Trained Language Models
Multilingual NLP and Cross-lingual Transfer Learning
Importance of Multilingual NLP
Challenges in Multilingual NLP
Techniques for Multilingual NLP
Cross-Lingual Transfer Learning
Building Multilingual Models with PyTorch
Explainable AI in NLP
Techniques for Explainable AI in NLP
Integration of NLP with Computer Vision
Importance of Integrating NLP with CV
Techniques for Integrating NLP with CV
Reinforcement Learning for NLP
Importance of Reinforcement Learning in NLP
Key Concepts in Reinforcement Learning
Common Applications of RL in NLP
Implementing RL-Based NLP Models with PyTorch
Conclusion
Points to Remember
Multiple Choice Questions
Answers
Questions
Key Terms
Index
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