Applied Natural Language Processing with PyTorch 2.0КНИГИ » ПРОГРАММИНГ
Название: 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 Автор: Deepti Chopra Издательство: Orange Education Pvt Ltd, AVA Год: 2025 Страниц: 204 Язык: английский Формат: epub (true) Размер: 10.1 MB
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!
Chapter 1. discusses an Introduction to Natural Language Processing (NLP). The topics covered in this chapter are about NLP, its applications, and various Challenges and Approaches in NLP. Chapter 2. discusses about PyTorch 2.0. Various topics covered in this chapter are introduction to PyTorch 2.0, Installation of PyTorch 2.0, PyTorch Basics, Tensors and Operations and GPU Acceleration with PyTorch. Chapter 3. discusses Text Preprocessing. Various topics covered include Tokenization, Stop Word Removal, Stemming and Lemmatization, Handling Special Characters and Punctuation, Word Embeddings and Word2Vec. Chapter 4. discusses Building NLP Models with PyTorch. This chapter covers Text Classification, Sentiment Analysis, Named Entity Recognition (NER), Part-of-Speech (POS) Tagging, Machine Translation, and Text Generation with Recurrent Neural Networks (RNNs) Chapter 5. discusses Advanced znLP Techniques with PyTorch. This chapter covers Sequence-to-Sequence Models, Attention Mechanisms, Transformer Models,T ransfer Learning for NLP and Language Modeling with GPT-3.5 Chapter 6. discusses Model Training and Evaluation. This chapter covers Dataset Preparation, Training Pipelines, Model Evaluation Metrics, Hyperparameter Tuning, Overfitting and Regularization Techniques. Chapter 7. discusses Improving NLP Models with PyTorch 2.0. This chapter covers Handling Out-of-Vocabulary (OOV) Words, Handling Long Sequences, Batch Processing and Data Loaders, Advanced Optimization Techniques, Model Interpretability, and Explainability. Chapter 8. discusses Deployment and Productionization. This chapter discusses about Exporting PyTorch Models, Deployment Strategies (Server, Edge, Cloud), Scaling and Performance Optimization, Monitoring and Debugging, and Ethical Considerations in NLP. Chapter 9. discusses Case Studies and Practical Examples. This chapter discusses Sentiment Analysis on Social Media Data, Text Classification for News Articles, Chatbot Development with PyTorch, Neural Machine Translation System, and Question Answering Systems. Chapter 10. discusses Future Trends in NLP and PyTorch. This chapter discusses Advances in Pretrained Language Models, Multilingual NLP and Cross-Lingual Transfer Learning, Explainable AI in NLP, Integration of NLP with Computer Vision, and Reinforcement Learning for NLP.
Скачать Applied Natural Language Processing with PyTorch 2.0
Навигация
Вход на сайт
Реклама
Applied Natural Language Processing with PyTorch 2.0 
