Description: Machine Learning for Biomedical Applications : With Scikit-learn and Pytorch, Paperback by Deprez, Maria; Robinson, Emma C., ISBN 0128229047, ISBN-13 9780128229040, Like New Used, Free shipping in the US 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 nots 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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Book Title: Machine Learning for Biomedical Applications : With Scikit-learn
Number of Pages: 326 Pages
Language: English
Publication Name: Machine Learning for Biomedical Applications : with Scikit-Learn and Pytorch
Publisher: Elsevier Science & Technology
Publication Year: 2023
Subject: Neural Networks, Intelligence (Ai) & Semantics, General, Computer Vision & Pattern Recognition
Type: Textbook
Subject Area: Computers, Medical
Author: Maria Deprez, Emma C. Robinson
Item Length: 9.2 in
Item Width: 7.5 in
Format: Trade Paperback