Applied Natural Language Processing with PyTorch 2.0
Applied Natural Language Processing with PyTorch 2.0
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分類：英文書＞自然科普＞電腦資訊＞網路/網路安全追蹤? 追蹤分類後，您會在第一時間收到分類新品通知。
- 作者： Deepti,Chopra 追蹤 ? 追蹤作者後，您會在第一時間收到作者新書通知。
- 出版社： Orange Education Pvt Ltd 追蹤 ? 追蹤出版社後，您會在第一時間收到出版社新書通知。
- 出版日：2025/01/27
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內容簡介
Unlock the Power of PyTorch 2.0 for Next-Level Natural Language Processing. Discover how to bring applied natural language processing with PyTorch 2.0 to life and gain proficiency in advanced NLP techniques for scalable AI models. This comprehensive, easy-to-follow guide is packed with real-world text classification and sentiment analysis projects, step-by-step instructions for machine translation and text generation, and best practices for training and evaluating NLP models with PyTorch. 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'll Learn Inside: Implement sequence-to-sequence models in PyTorch 2.0 for neural network text solutionsStep-by-step lessons on sentiment analysis in Python and text classification with PyTorch to solve real business challengesComprehensive applied NLP guide covering preprocessing text data for neural architecturesActionable examples of named entity recognition, information extraction, and NLP case studiesMaster transformer models and pre-trained language models in NLP for state-of-the-art resultsInsights on building and tuning deep learning NLP pipelines for practical deploymentsSee future trends and innovations in Python NLP books for continued skill developmentWho Should Read This Book?Data scientists, engineers, and developers searching for an up-to-date PyTorch NLP book and applied NLP guidePython enthusiasts eager to apply sentiment analysis, machine translation NLP, and sequence models in real projectsProfessionals and students seeking to master neural network text understanding and deep learning solutions.Why This Guide?All information is authentic and policy-compliant-no unauthorized brands or characters are usedOptimized for Amazon search and human readability, using established keywords for higher ranking and conversionWritten in a clear, natural style suited for both advanced and beginning practitioners
Start mastering applied NLP techniques with PyTorch 2.0-build scalable, production-ready AI models today! 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
Start mastering applied NLP techniques with PyTorch 2.0-build scalable, production-ready AI models today! 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
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