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Machine Learning for Biomedical Applications

Bag om Machine Learning for Biomedical Applications

Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9780128229040
  • Indbinding:
  • Paperback
  • Sideantal:
  • 304
  • Udgivet:
  • 13. september 2023
  • Størrelse:
  • 237x191x18 mm.
  • Vægt:
  • 636 g.
  På lager
Leveringstid: 4-7 hverdage
Forventet levering: 11. december 2024
Forlænget returret til d. 31. januar 2025

Beskrivelse af Machine Learning for Biomedical Applications

Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.

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