Practical Natural Language Processing
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About
Practical Natural Language Processing is a guide designed for software engineers, data scientists, machine learning engineers, product managers, and business leaders to build, iterate, and scale natural language processing (NLP) systems. Authored by experts in NLP, the book provides insight into various techniques, best practices, and caveats in deploying NLP applications across different verticals such as healthcare, social media, and retail. It offers a breadth of concepts with over 450 references to assist in understanding different algorithms and building effective NLP solutions. Endorsed by AI industry leaders, the book caters particularly to the needs of practitioners and business professionals, making it accessible for readers at different levels of expertise in the field. The authors have significant experience in both academic and industry settings, further enhancing the book's credibility as a reference for practical NLP applications. Additionally, an accompanying code repository provides evolving resources for implementing the techniques discussed.
Platform
Task
Features
• comprehensive resources including over 450 references
• implementation of nlp applications with machine learning and deep learning
• use of best practices around release and deployment
• adaptation for various industry verticals
• guidance on building real-world nlp systems
FAQs
What is the target audience for this book?
The book is written for software engineers, data scientists, machine learning engineers, product managers, and business leaders interested in NLP.
Does the book cover advanced theoretical concepts in NLP?
The book focuses on practical applications and may not cover in-depth theoretical concepts, catering more to practitioners.
Is there a repository for the book's code examples?
Yes, the code for the book is available in a publicly accessible repository that is regularly updated.
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