Maria Deprez, Emma C. Robinson

Machine Learning for Biomedical Applications (eBook, ePUB)

With Scikit-Learn and PyTorch

• Format: ePub

Produktbeschreibung

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.

• Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis
• Shows how to apply a range of commonly used machine learning and deep learning techniques in biomedical problems
• Develops practical computational skills that are needed to manipulate complex biomedical data sets
• Shows how to design machine learning experiments that address specific problems related to biomedical data

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 326
• Erscheinungstermin: 7. September 2023
• Englisch
• ISBN-13: 9780128229057
• Artikelnr.: 68878916

Autorenporträt

Dr Maria Deprez is a Lecturer in Medical Imaging in the Department of Perinatal Imaging & Health at the School of Biomedical Engineering & Imaging Sciences. Her Research interests are in motion correction and reconstruction of fetal and placental MRI, Spatio-temporal models of developing brain, segmentation, registration, atlases, machine learning, and deep learning

Inhaltsangabe

1. Programming in Python
2. Machine Learning Basics
3. Regression
4. Classification
5. Dimensionality reduction
6. Clustering
7. Ensemble methods
8. Feature extraction and selection
9. Introduction to Deep Learning
10. Neural Networks
11. Convolutional Neural Networks