Titel: MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities
Verlag: IGI Global, US
Erscheinungsjahr: 2020
Sprache: Englisch
ISBN-10: 1799815552
ISBN-13: 9781799815556
Einband: Paperback
Zustand: New

�ber diesen Titel

Inhaltsangabe

Deep learning has become a trending area of research due to its adaptive characteristics and high levels of applicability. In recent years, researchers have begun applying deep learning strategies to image analysis and pattern recognition for solving technical issues within image classification. As these technologies continue to advance, professionals have begun translating this intelligent programming language into mobile applications for devices. Programmers and web developers are in need of significant research on how to successfully develop pattern recognition applications using intelligent programming.

MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities is an essential reference source that presents a solution to developing intelligent pattern recognition Apps on iOS devices based on MatConvNet deep learning. Featuring research on topics such as medical image diagnosis, convolutional neural networks, and character classification, this book is ideally designed for programmers, developers, researchers, practitioners, engineers, academicians, students, scientists, and educators seeking coverage on the specific development of iOS mobile applications using pattern recognition strategies.

�ber die Autorinnen und Autoren

Jiann-Ming Wu is a mathematics educator in Taiwan. He currently serves National Dong Hwa University as a professor and head in the department of Applied Mathematics. In addition to holding a career in education, Dr. Jiann-Ming is known for the design of multilayer Potts perceptrons, generalized adalines, natural elastic nets, Mahalanobis-NRBF neural networks, state-regulated inverse neural networks, Potts ICA neural networks, Sudoku associative memory, and deep learning, inluding annealed Kullback-Leibler divergence minimization learning, annealed cooperative-competitive learning, hybrid mean-field-annealing and gradient descent deep learning, among others. Prior to entering a career in mathematics education, Dr. Jiann-Ming received a Bachelor of Science in Engineering and Computer Science from National Chiao Tung University in 1988. He went on to attend National Taiwan University, where he completed a Master of Science in Computer Science and Information Engineering in 1990 and a PhD in 1994. Dr. Jiann-Ming is certified in engineering through the International Neural Network Society.

Chao-Yuan Tien was born in Taiwan in 1995. He published Handwriting 99 Multiplication App on Apple’s App Store in 2018. He received the M.S. degree in applied mathematics from National Dong Hwa University, Hualien, Taiwan, in spring 2019, and the B.S. degree in applied mathematics from National Dong Hwa University, Hualien, Taiwan, in 2017. His research interesting includes Deep learning, MatConvNet deep learning, Caffe deep learning, iOS mobile App design.

��ber diesen Titel� kann sich auf eine andere Ausgabe dieses Titels beziehen.