Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Natural Language Processing with Spark Nlp: Learning to Understand Text at Scale

Natural Language Processing with Spark Nlp: Learning to Understand Text at Scale

Thomas, Alex

Paperback

DatabasesGeneral Computers

Publisher Price: $79.99

ISBN10: 1492047767
ISBN13: 9781492047766
Publisher: Oreilly Media
Published: Aug 4 2020
Pages: 364
Weight: 1.28
Height: 0.76 Width: 7.00 Depth: 9.19
Language: English

If you want to build an enterprise-quality application that uses natural language text but arenÃÂÂ[ t sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language processing (NLP) applications using deep learning and the Apache Spark NLP library.

Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. YouÃÂÂ[ ll also explore special concerns for developing text-based applications, such as performance.

In four sections, youÃÂÂ[ ll learn NLP basics and building blocks before diving into application and system building:

  • Basics: Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learning
  • Building blocks: Learn techniques for building NLP applicationsÃÂÂ[ including tokenization, sentence segmentation, and named-entity recognitionÃÂÂ[ and discover how and why they work
  • Applications: Explore the design, development, and experimentation process for building your own NLP applications
  • Building NLP systems: Consider options for productionizing and deploying NLP models, including which human languages to support

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