Pocket Primer Series Read Description

Natural Language Processing using R Pocket Primer

Paperback
February 2022
9781683927303
More details
  • Publisher
    Mercury Learning and Information
  • Published
    25th February
  • ISBN 9781683927303
  • Language English
  • Pages 246 pp.
  • Size 6" x 9"
$41.95
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January 2022
9781683927297
More details
  • Publisher
    Mercury Learning and Information
  • Published
    5th January
  • ISBN 9781683927297
  • Language English
  • Pages 246 pp.
  • Size 6" x 9"
$129.95
E-Book (ePub)
January 2022
9781683927280
More details
  • Publisher
    Mercury Learning and Information
  • Published
    5th January
  • ISBN 9781683927280
  • Language English
  • Pages 246 pp.
  • Size 6" x 9"
$34.95

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 (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, and TensorFlow. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning).

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