Deep learning with R: by A. Ghatak, Singapore, Springer, 2019, 245 pp., €89.99, ISBN 978-981-13-5849-4.

The article focuses on "Deep Learning with R," a monograph by Abhijit Ghatak that explores the application of deep learning techniques using the R programming language, particularly in the context of natural language processing (NLP). The book provides a comprehensive guide, starting from fundamenta...

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Publicado en:Innovation: The European Journal of Social Sciences Vol. 38; no. 3; pp. 1412 - 1416
Autores principales: Feng, Haoda, Liu, Hongqian
Formato: Book Review
Publicado: Taylor & Francis Ltd Sep2025
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Sep2025
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      pub: Taylor & Francis Ltd
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        atl: Deep learning with R: by A. Ghatak, Singapore, Springer, 2019, 245 pp., €89.99, ISBN 978-981-13-5849-4.
      aug:
        au:
          Feng, Haoda
          Liu, Hongqian
        affil:
          Dalian Maritime University, People's Republic of China
          Liaoning University of Technology, People's Republic of China
      su:
        Artificial intelligence
        Deep learning
        Natural language processing
        Mathematical functions
        Machine learning
        Artificial neural networks
        Programming languages
        Optimization algorithms
      sug:
        subj:
          Artificial intelligence
          Software Publishers
          Software publishers (except video game publishers)
          Deep learning
          Natural language processing
          Mathematical functions
          Machine learning
          Artificial neural networks
          Programming languages
          Optimization algorithms
      ab: The article focuses on "Deep Learning with R," a monograph by Abhijit Ghatak that explores the application of deep learning techniques using the R programming language, particularly in the context of natural language processing (NLP). The book provides a comprehensive guide, starting from fundamental concepts of machine learning and neural networks to practical applications in NLP, making it suitable for a wide audience, including novice researchers and experienced engineers. It covers various types of neural networks, optimization algorithms, and practical coding examples, emphasizing the advantages of using R for NLP tasks. The text highlights the book's structured approach and accessibility, making it a valuable resource for those interested in deep learning and AI engineering.
      pubtype: Review
      doctype: Book Review
      src: R
    language: English
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