Envío gratis siempre a todo México.
Haz clic para hacer zoom
Not Available
Meta Learning With Medical Imaging and Health Informatics Applications
Cargando comentarios…
Comparte
$1,728
$1,555
Envío gratis siempre
Comentarios
Cargando el resumen…
Cargando comentarios…
product
1999272Meta Learning With Medical Imaging and Health Informatics Applicationshttps://www.gandhi.com.mx/meta-learning-with-medical-imaging-and-health-informatics-applications/phttps://gandhi.vtexassets.com/arquivos/ids/885847/92d0cea5-833e-41be-9ef4-c773deca242c.jpg?v=63833662020753000015551728MXNAcademic PressInStock/Ebooks/<p>Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. - First book on applying Meta Learning to medical imaging - Pioneers in the field as contributing authors to explain the theory and its development - Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly</p>...1959736Meta Learning With Medical Imaging and Health Informatics Applications15551728https://www.gandhi.com.mx/meta-learning-with-medical-imaging-and-health-informatics-applications/phttps://gandhi.vtexassets.com/arquivos/ids/885847/92d0cea5-833e-41be-9ef4-c773deca242c.jpg?v=638336620207530000InStockMXN99999DIEbook20229780323998529_W3siaWQiOiI2ZDZjZmM5Yi1mYTY0LTQ1ZmQtODFhOC1iYTNlZWY5MDEwYjQiLCJsaXN0UHJpY2UiOjE3MjgsImRpc2NvdW50IjoxNzMsInNlbGxpbmdQcmljZSI6MTU1NSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjUtMDEtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9780323998529_<p>Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, <em>Meta-Learning</em> constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.</p><p>This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions.</p><ul><li>First book on applying Meta Learning to medical imaging</li><li>Pioneers in the field as contributing authors to explain the theory and its development</li><li>Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly</li></ul>...(*_*)9780323998529_<p>Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. - First book on applying Meta Learning to medical imaging - Pioneers in the field as contributing authors to explain the theory and its development - Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly</p>...9780323998529_Elsevier Science(*_*)9780323998529_Academic Presslibro_electonico_77a12b59-b00d-3bca-8e41-611e1f614cb3_9780323998529;9780323998529_9780323998529InglésMéxicoAcademic Presshttps://getbook.kobo.com/koboid-prod-public/elsevierrefmonographs-epub-96eb41b0-f436-4879-933b-69f3ca0d529a.epub2022-09-24T00:00:00+00:00