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008			221118s2022 sz | s |||| 0|eng d
020			_a9783031195020
024	7		_a10.1007/978-3-031-19502-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aR859.7.A78 _b2022 EB
100	1		_aBorhani, Reza _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685867
245	1	0	_aFundamentals of Machine Learning and Deep Learning in Medicine _cby Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XI, 196 páginas) _b122 ilustraciones, 89 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aIntroduction -- Mathematical Modeling of Medical Data -- Linear Learning -- Nonlinear Learning -- Multi-Layer Perceptrons -- Convolutional Neural Networks -- Recurrent Neural Networks -- Autoencoders -- Generative Adversarial Networks -- Reinforcement Learning.
520			_aThis book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader's learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites. .
988			_aSpringer_Medicine_2022
650		7	_2embne _9421371 _aInteligencia artificial en medicina
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aBorhani, Soheila _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685868
700	1		_aKatsaggelos, Aggelos Konstantinos, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685869 _d1956-
776	0	8	_iPrinted edition: _z9783031195013
776	0	8	_iPrinted edition: _z9783031195037
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-19502-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b12/2022 _dz _esc _zSI