ISBN: 9780323998512 - meta learning with medical imaging and health informatics applications (19 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Condizione: New. 2022. 1st Edition. Paperback. . . . . .

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Paperback / softback. Condizione: New. New copy - Usually dispatched within 3 working days.

Condizione: New. In English.

Paperback. Condizione: Brand New. 375 pages. 9.00x7.25x0.75 inches. In Stock.

Paperback. Condizione: New. 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.…

Condizione: New. 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 th.

Condizione: New. 2022. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Condizione: Neu. Neuware - 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.…

Taschenbuch. Condizione: Neu. Meta Learning With Medical Imaging and Health Informatics Applications | Hien van Nguyen (u. a.) | Taschenbuch | The MICCAI Society book Series | Einband - flex.(Paperback) | Englisch | 2022 | Elsevier Science & Technology | EAN 9780323998512 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.…

Paperback. Condizione: New. 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.…

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