Natural Language Processing using R Pocket Primer
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Natural Language Processing using R Pocket Primer

Oswald Campesato
Paperback
February 2022
9781683927303
More details
  • Publisher
    Mercury Learning and Information
  • Published
    25th February 2022
  • ISBN 9781683927303
  • Language English
  • Pages 246 pp.
  • Size 6" x 9"
$41.95
Add to Cart
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January 2022
9781683927280
More details
  • Publisher
    Mercury Learning and Information
  • Published
    5th January 2022
  • ISBN 9781683927280
  • Language English
  • Pages 246 pp.
  • Size 6" x 9"
$41.95
Add to Cart
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January 2022
9781683927297
More details
  • Publisher
    Mercury Learning and Information
  • Published
    5th January 2022
  • ISBN 9781683927297
  • Language English
  • Pages 246 pp.
  • Size 6" x 9"
$129.95

This book is for developers who are looking for an overview of basic concepts in Natural Language Processing using R. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The final chapter presents the Transformer Architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years. Companion files with source code and figures are included and available for downloading by emailing the publisher at info@merclearning.com with proof of purchase.

FEATURES:

  • Covers extensive topics related to natural language processing using R

  • Features companion files with source code and figures from the book

1: Introduction to R

2: Conditionals, Loops, and Data Frames

3: Working with Functions in R

4. NLP Concepts (I)

5. NLP Concepts (II)

6: NLP and R

7: Transformer, BERT, and GPT

Appendices

Index

Oswald Campesato

Oswald Campesato specializes in Deep Learning, Python, Data Science, and generative AI. He is the author/co-author of over forty-five books including Google Gemini for Python, Large Language Models, and GPT-4 for Developers (all Mercury Learning).

Computer Science; Data Analytics; Natural Language Processing; Machine Learning; R