ISBN: 9781032544717 - quantum machine learning: a modern approach (20 risultati)

Condizione: very_good.

hardcover. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. 1st edition NO-PA16APR2015-KAP.

Condizione: New.

Condizione: New. In English.

Hardback. Condizione: New. This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies.In summary, the book: Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects.This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.…

Hardcover. Condizione: Brand New. 304 pages. 9.18x6.12x9.21 inches. In Stock.

Hardback. Condizione: New. This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies.In summary, the book: Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects.This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr. Karthikeyan Saminathan is currently working as an Associate Professor(Research) in the Department of Cyber Security at Sri Venkateshwara College of Engineering, Bengaluru, Karnataka, India and He is associated with Bluechip CyberTech. …

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies.In summary, the book: Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering. 300 pp. Englisch.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies.In summary, the book: Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.…