Machine Learning for Biomedical Applications by Maria …, Pasir Ris
Machine Learning for Biomedical Applications by Maria …, Pasir Ris
$144.41
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Pasir Ris
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Ships from and sold by Pick A Book Asia Singapore
Publisher: Academic Press
ISBN 13: 9780128229040
Condition: Brandnew
Binding: paperback
Pages: 300
Dimensons: 198 x 132 x 30 | 400 (gram)
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Machine Learning for Biomedical Applications 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 solving simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. The book is divided into four Parts: A general background to machine learning techniques and their use in biomedical applications, practical Python coding skills, and mathematical tool that underpin the field; core machine learning methods; Deep learning concepts with examples in Keras.; tricks of the trade where guidance is given on best practice for data preparation and experimental design to aid the successful application of machine learning methods to real world biomedical data. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, biomedical science, and clinicians.
Publisher: Academic Press
ISBN 13: 9780128229040
Condition: Brandnew
Binding: paperback
Pages: 300
Dimensons: 198 x 132 x 30 | 400 (gram)
----------------------------------------
Machine Learning for Biomedical Applications 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 solving simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. The book is divided into four Parts: A general background to machine learning techniques and their use in biomedical applications, practical Python coding skills, and mathematical tool that underpin the field; core machine learning methods; Deep learning concepts with examples in Keras.; tricks of the trade where guidance is given on best practice for data preparation and experimental design to aid the successful application of machine learning methods to real world biomedical data. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, biomedical science, and clinicians.
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