000			03008nam a22002057a 4500
008			250225b ||||| |||| 00| 0 eng d
020			_a9781119821892
040			_aAR-FvUNAJ
041			_aeng
100	1		_921272 _aSingh, Pradeep _eed.
245	1	0	_aFundamentals and methods of machine and deep learning : _balgorithms, tools, and applications / _cEditor Pradeep Singh
260			_aBeverly, MA : _bWiley-Scrivener, _c2022.
300			_axx, 445 p.
500			_aDescargar, imprimir, guardar y enviar por correo electrónico 60 páginas permitidas. Descarga completa del libro electrónico. Debe tener instalado Adobe Digital Editions para leer el eBook.
520			_aFUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers. Authors
650		0	_911553 _aAPRENDIZAJE AUTOMÁTICO
856			_zIngresa con la contraseña de EBSCO _uhttps://research.ebsco.com/c/6n4nn2/search/details/pkim2aosiz?db=nlebk&limiters=None&q=Fundamentals%20and%20methods%20of%20machine
942			_2ddc _cLIBRO DIGI
980			_62 _aVirginia Figueroa _82 _gVirginia Figueroa
999			_c10747 _d10747