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Machine Learning Foundations - Taeho Jo - Bog

Bag om Machine Learning Foundations

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783030658991
  • Indbinding:
  • Hardback
  • Sideantal:
  • 391
  • Udgivet:
  • 13. februar 2021
  • Udgave:
  • 12021
  • Størrelse:
  • 155x235x0 mm.
  • Vægt:
  • 781 g.
  • 8-11 hverdage.
  • 19. november 2024

Normalpris

  • BLACK NOVEMBER

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

Beskrivelse af Machine Learning Foundations

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning.
Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning;
Outlines the computation paradigm for solving classification, regression, and clustering;
Features essential techniques for building the a new generation of machine learning.

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