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 to biomedical problems. - Develops practical computational skills needed to implement machine learning and deep learning models for biomedical data sets. - Shows how to design machine learning experiments that address specific problems related to biomedical data
Mit "türkischem" Urum, pontischem "Griechisch" und orthodox-christlichem Glauben unterläuft Georgiens griechische Minderheit gängige Erwartungen an das Verhältnis von Sprache und (nationaler) Identität. In Georgien als griechisch anerkannt, in Griechenland jedoch nicht unbedingt, bewegen sie sich in einem spannungsreichen Geflecht sozialer Konstellationen und (un)möglicher Zugehörigkeiten, geprägt von Spuren der sowjetischen Vergangenheit. In einer sorgfältigen ethnografisch informierten Konversationsanalyse untersucht die Autorin die Aushandlung komplexer sozialer Grenzen, Zugehörigkeiten und Positionierungen im Gespräch. Grenzziehungen und -auflösungen erweisen sich dabei als dynamische und kontextabhängige Prozesse.
Collegiality is a core legal principle of the European Commission's internal decision-making, acting as a safeguard to the Commission's supranational character and ensuring the Commission's independence from EU Member States. Despite collegiality's central role within the Commission, its legal and political implications have remained critically underexamined. Collegiality in the European Commission sheds light on this crucial aspect of the Commission's work for the first time. In this novel study on collegiality, Maria Patrin proposes an innovative framework for assessing the Commission's institutional role and power. The book's first part legally examines collegiality, retracing collegial procedures and actors in different layers of decision-making -- from the Commission's services to the College of Commissioners. The second part of the book explores the implementation of collegiality through illustrative case studies, focusing on various Commission functions including legislative initiative, infringement proceedings, and economic governance. Partin's empirical analysis unveils a disconnect between the legal notion of collegiality and its concrete application in institutional practices. These variations raise normative questions on how to ensure the unity of the Commission as a collegial body despite the diversification of decision-making functions. They also invite a re-examination of the Commission's multifaceted role in the current EU institutional, legal, and political setting. Adopting an interdisciplinary approach that delves into both the legal substance and the political-institutional practice of collegiality, this book offers a unique, behind-the-scenes insight into the Commission's decision-making processes, furthering our understanding of the EU's institutional system.
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 to biomedical problems. - Develops practical computational skills needed to implement machine learning and deep learning models for biomedical data sets. - Shows how to design machine learning experiments that address specific problems related to biomedical data
Each person and household experiences poverty and inequality differently, and so listening deeply to many people's experiences lays the foundation for learning together about options and choices. SenseMaker is a unique method of inquiry that encourages and enables novel insights not obtained from conventional quantitative and qualitative methods.
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