Practical Natural Language Processing
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About
Practical Natural Language Processing (PNLP) is a comprehensive guide for building, iterating, and scaling NLP systems across various industry verticals. It helps practitioners navigate the complex landscape of NLP solutions, from basic rule-based systems to advanced deep learning techniques, adapting them for fields like healthcare, social media, and retail. The book covers analyzing health records, detecting fake news, and extracting e-commerce product attributes. PNLP is designed for software engineers, data scientists, machine learning engineers, product managers, and business leaders who need to apply cutting-edge NLP in real-world products and ventures. It provides over 450 references and covers best practices for release, deployment, and DevOps in NLP systems.
Platform
Features
• understand best practices, opportunities, and the roadmap for nlp from a business and product leader’s perspective
• produce software solutions following best practices for release, deployment, and devops
• evaluate various algorithms and approaches for nlp product tasks
• fine-tune nlp solutions based on business problems and industry verticals
• implement and evaluate different nlp applications using ml and deep learning methods
• understand nlp problem statements, tasks, and solution approaches
FAQs
Can print buyers view the color version?
Higher-quality color versions of all images are available for each chapter in our repository and can be downloaded from practical-nlp-figures.
Where can we view and submit errata for the book?
The errata is accessible on the O'Reilly website, and requests for technical or grammatical changes can also be submitted there.
Does this book have an accompanying code repository?
Yes, the accompanying code for the book is publicly available and constantly evolving in our GitHub repository.
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