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This work provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses applications of ML to various neurological and psychiatric disorders.
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Deep learning is a branch of machine learning similar to artificial intelligence. The applications of deep learning vary from medical imaging to industrial quality checking, sports, and precision agriculture. This book is divided into two sections. The first section covers deep learning architectures and the second section describes the state of the art of applications based on deep learning.
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What distinguishes an adventure novel from a historical novel? Can the same text belong to several genres? More to one than to another? Have some existing genres been overlooked? To answer these and similar questions, José Calvo Tello combines methods from Linguistics (lexicography), Literary Studies (genre theory), and Computer Science (machine learning, natural language processing). Located in the interdisciplinary field of Digital Humanities, this study analyzes a newly developed corpus of 358 Spanish novels of the silver age (1880-1939), which includes authors like Baroja, Pardo Bazán, or Valle-Inclán. Calvo Tello's key result is a graph-based model of literarygenre that reconciles recent theoretical approaches.
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