LEADER 04536nam 22006735 450 001 9910631085603321 005 20260122154950.0 010 $a3-031-19502-7 024 7 $a10.1007/978-3-031-19502-0 035 $a(MiAaPQ)EBC7143426 035 $a(Au-PeEL)EBL7143426 035 $a(CKB)25401998400041 035 $a(PPN)266356796 035 $a(OCoLC)1353300260 035 $a(DE-He213)978-3-031-19502-0 035 $a(EXLCZ)9925401998400041 100 $a20221118d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFundamentals of Machine Learning and Deep Learning in Medicine /$fby Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (201 pages) 225 1 $aMedicine Series 311 08$aPrint version: Borhani, Reza Fundamentals of Machine Learning and Deep Learning in Medicine Cham : Springer International Publishing AG,c2022 9783031195013 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. . 410 0$aMedicine Series 606 $aInternal medicine 606 $aMachine learning 606 $aInternal Medicine 606 $aMachine Learning 606 $aAprenentatge automàtic$2thub 606 $aIntel·ligència artificial$2thub 606 $aÚs terapèutic$2thub 608 $aLlibres electrònics$2thub 615 0$aInternal medicine. 615 0$aMachine learning. 615 14$aInternal Medicine. 615 24$aMachine Learning. 615 7$aAprenentatge automàtic 615 7$aIntel·ligència artificial 615 7$aÚs terapèutic 676 $a006.31 676 $a610.285631 700 $aBorhani$b Reza$01264715 702 $aKatsaggelos$b Aggelos Konstantinos$f1956- 702 $aBorhani$b Soheila 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910631085603321 996 $aFundamentals of Machine Learning and Deep Learning in Medicine$92965641 997 $aUNINA