SpaCy for Natural Language Processing (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"SpaCy for Natural Language Processing" "SpaCy for Natural Language Processing" is a comprehensive guide to mastering one of the industry's leading natural language processing (NLP) frameworks. Through a carefully structured progression, the book introduces SpaCy's core philosophies and modern architecture, offering a foundation for readers who seek both theoretical knowledge and practical expertise. The opening chapters dissect SpaCy's essential data structures, advanced pipeline mechanics, and provide clear guidance on installation, environment management, and compatibility across platforms....
"SpaCy for Natural Language Processing"
"SpaCy for Natural Language Processing" is a comprehensive guide to mastering one of the industry's leading natural language processing (NLP) frameworks. Through a carefully structured progression, the book introduces SpaCy's core philosophies and modern architecture, offering a foundation for readers who seek both theoretical knowledge and practical expertise. The opening chapters dissect SpaCy's essential data structures, advanced pipeline mechanics, and provide clear guidance on installation, environment management, and compatibility across platforms. The text also situates SpaCy within the broader NLP landscape with thorough comparisons to frameworks like NLTK, CoreNLP, Stanza, and Transformers.
The book delves deeply into the mechanics of tokenization, segmentation, and linguistic annotation, equipping practitioners to handle challenging multilingual and large-scale data scenarios. Readers will explore state-of-the-art workflows for part-of-speech tagging, morphological analysis, dependency parsing, and named entity recognition, with an emphasis on extensibility, error analysis, and annotation best practices. A significant focus is given to building and customizing NLP pipelines, covering topics such as crafting custom components, integrating statistical and rule-based logic, profiling for performance, and deploying robust pipelines at scale.
Advanced chapters address the full lifecycle of model development: from data preparation and model training to fine-tuning, deployment, and integration with machine learning libraries like scikit-learn and Transformers. Cutting-edge topics-active learning, explainability, privacy, on-device deployment, and benchmarking-are explored alongside guidance for maintaining production-grade workflows. The concluding chapters encourage readers to adopt best practices, contribute to the evolving SpaCy ecosystem, and reflect critically on ethical and responsible AI in NLP, making this book a vital resource for engineers, researchers, and forward-thinking NLP practitioners.
"SpaCy for Natural Language Processing" is a comprehensive guide to mastering one of the industry's leading natural language processing (NLP) frameworks. Through a carefully structured progression, the book introduces SpaCy's core philosophies and modern architecture, offering a foundation for readers who seek both theoretical knowledge and practical expertise. The opening chapters dissect SpaCy's essential data structures, advanced pipeline mechanics, and provide clear guidance on installation, environment management, and compatibility across platforms. The text also situates SpaCy within the broader NLP landscape with thorough comparisons to frameworks like NLTK, CoreNLP, Stanza, and Transformers.
The book delves deeply into the mechanics of tokenization, segmentation, and linguistic annotation, equipping practitioners to handle challenging multilingual and large-scale data scenarios. Readers will explore state-of-the-art workflows for part-of-speech tagging, morphological analysis, dependency parsing, and named entity recognition, with an emphasis on extensibility, error analysis, and annotation best practices. A significant focus is given to building and customizing NLP pipelines, covering topics such as crafting custom components, integrating statistical and rule-based logic, profiling for performance, and deploying robust pipelines at scale.
Advanced chapters address the full lifecycle of model development: from data preparation and model training to fine-tuning, deployment, and integration with machine learning libraries like scikit-learn and Transformers. Cutting-edge topics-active learning, explainability, privacy, on-device deployment, and benchmarking-are explored alongside guidance for maintaining production-grade workflows. The concluding chapters encourage readers to adopt best practices, contribute to the evolving SpaCy ecosystem, and reflect critically on ethical and responsible AI in NLP, making this book a vital resource for engineers, researchers, and forward-thinking NLP practitioners.
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