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Deep Learning Foundations - Taeho Jo - Bog

Bag om Deep Learning Foundations

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book¿s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031328787
  • Indbinding:
  • Hardback
  • Sideantal:
  • 448
  • Udgivet:
  • 26. juli 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 160x30x241 mm.
  • Vægt:
  • 834 g.
  • 8-11 hverdage.
  • 10. december 2024
På lager

Normalpris

  • BLACK WEEK

Medlemspris

Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Deep Learning Foundations

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book¿s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

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