Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch by M

Description: Machine Learning for Biomedical Applications by Maria Deprez, Emma C. Robinson 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Author Biography 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 Dr Robinsons research focuses on the development of computational methods for brain imaging analysis, and covers a wide range of image processing and machine learning topics. Most notably, her software for cortical surface registration (Multimodal Surface Matching, MSM) has been central to the development of ?of the Human Connectome Projects "Multi-modal parcellation of the Human Cortex " (Glasser et al, Nature 2016), and has featured as a central tenet in the HCPs paradigm for neuroimage analysis (Glasser et al, Nature NeuroScience 2016). This work has been widely reported in the media including Wired, Scientific American, and Wall Street Journal). Current research interests are focused on the application of advanced machine learning, and particularly Deep Learning to diverse data sets combining multi-modality imaging data with genetic samples. Table of Contents 1. Programming in Python2. Machine Learning Basics3. Regression4. Classification5. Dimensionality reduction6. Clustering7. Ensemble methods8. Feature extraction and selection9. Introduction to Deep Learning10. Neural Networks11. Convolutional Neural Networks Feature Gives a basic understanding of the most fundamental concepts within machine learning and their role in biomedical data analysis Shows to apply a range of commonly used machine learning and deep learning techniques to 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 Details ISBN0128229047 Author Emma C. Robinson Language English ISBN-10 0128229047 ISBN-13 9780128229040 Format Paperback Publisher Elsevier Science Publishing Co Inc Imprint Academic Press Inc Place of Publication San Diego Country of Publication United States Year 2023 UK Release Date 2023-09-01 Subtitle With Scikit-Learn and PyTorch Alternative 9780128229057 DEWEY 610.285631 Audience Tertiary & Higher Education Pages 304 NZ Release Date 2023-09-11 AU Release Date 2023-09-11 Publication Date 2023-09-13 US Release Date 2023-09-13 Illustrations 84 illustrations (48 in full color); Illustrations We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:145251691;

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