Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Februar 2022
Genre:	EDV / Informatik
	Agricultural Engineering / Agriculture / AI / Artificial Intelligence / computer science / Deep Learning / Electrical & Electronics Engineering / Elektrotechnik u. Elektronik / Informatik / Intelligent Systems & Agents / Intelligente Systeme u. Agenten / KI / Künstliche Intelligenz / Landwirtschaft / Landwirtschaftstechnik / Maschinelles Lernen
ISBN:	9781119821250
EAN-Code:	9781119821250
Verlag:	Wiley
Einband:	Gebunden
Sprache:	English
Dimensionen:	H 239 mm / B 161 mm / D 30 mm
Gewicht:	786 gr
Seiten:	480
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

FUNDAMENTALS 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.