Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand (Paperback)
This hands-on guide helps you get up to speed on the latest and most promising trends in NLP.
Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand (Paperback)
Numéro d'article: 37686625

Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand (Paperback)

Numéro d'article: 37686625
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This hands-on guide helps you get up to speed on the latest and most promising trends in NLP.
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Ce qui se démarque

Comprehensive Coverage
This book provides an extensive exploration of natural language processing, ensuring readers grasp both foundational and advanced concepts essential for practical implementation in enterprise settings.
Business Applications
Focusing on real-world use cases, this resource equips professionals with the knowledge to seamlessly integrate NLP technologies into business strategies, enhancing operational efficiency and decision-making capabilities.
Expert Insights
Authored by industry leaders, the book offers unparalleled perspectives on the challenges and opportunities of applying NLP in enterprises, making it a valuable asset for both novices and seasoned practitioners.

Détails du produit

Shop Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand (Paperback) online at a best price in Djibouti. 785377671
  • Authors: Ankur A Patel and Ajay Uppili Arasanipalai
  • ISBN: 9781492062578
  • Format: Paperback
  • Publication Date: 2021-05-11
  • Learn how to use state-of-the-art NLP models such as BERT and GPT-3 for tasks like named entity recognition, text classification, semantic search, and reading comprehension
  • Build, train, and deploy NLP models with performance comparable or superior to out-of-the-box systems using Python and PyTorch
  • Get familiar with tools like spaCy, Hugging Face, and fast.ai, and learn about modern NLP tricks like transfer learning and Transformer architecture
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreComputing & Internet
Publication dateJune, 2021
Pages333
Reading levelProfessional and Scholarly
SubgenreComputers
Edition1st Edition
PublisherO'Reilly Media
Original languagesEnglish
LanguageEnglish
Assembled product height9.19 in (23.3 cm)
Assembled product weight1.18 lb (540 grams)
Bisac subject headingComputers

À qui est-ce destiné ?

Suitable For
  • Data Scientists

    Ideal for data scientists seeking to enhance machine learning models with advanced natural language processing techniques.

  • Business Analysts

    Great for business analysts looking to leverage NLP tools for data interpretation and decision-making in enterprises.

  • Software Developers

    Beneficial for software developers interested in integrating NLP solutions into applications and understanding the underlying algorithms.

Not Suitable For
  • Casual Readers

    Not suitable for casual readers due to its technical nature and focus on enterprise applications of NLP.

DESCRIPTION DU PRODUIT

Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand (Paperback)

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: Do I need prior knowledge of NLP to use this guide?

    répondre: A basic understanding of machine learning and some Python experience is recommended.
  • question: What NLP tasks will I be able to tackle after this guide?

    répondre: You will be equipped to handle tasks like named entity recognition, text classification, and semantic search.
  • question: What tools will I learn to use?

    répondre: You will learn to use powerful tools such as spaCy, Hugging Face, and fast.ai.

Ankur Patel; Ajay Arasanipalai All Books Editorial Review

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Avantages

  • Comprehensive coverage of NLP
  • Practical applications discussed
  • Clear and engaging writing style
  • Valuable for both beginners and experts
  • Real-world case studies included

Les inconvénients

  • Some sections could be more in-depth.

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