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Añadir al carritoPaperback. Condición: Brand New. 348 pages. 9.18x6.12x9.21 inches. In Stock.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2026
ISBN 10: 1041144652 ISBN 13: 9781041144656
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Añadir al carritoPaperback. Condición: new. Paperback. In the exploration of new frontiers in data-driven solutions, the potential of quantum-enhanced machine learning has become too important to overlook. Quantum machine learning, though still in its formative stages, holds the promise to tackle some of the most complex problems that lie beyond the reach of classical computing. Quantum Machine Learning: Concepts, Algorithms, and Applications is a guide to understanding such quantum principles as superposition and entanglement and how they can enhance learning algorithms and data-processing capabilities. The book features a carefully structured progression from foundational concepts and core algorithms to application-driven case studies and emerging directions for future exploration.The book provides a broad and in-depth treatment of topics ranging from quantum data encoding and quantum neural networks to hybrid models and optimization frameworks. Emphasis has also been placed on real-world use cases and the practical tools available for implementation, thereby ensuring that this book serves not only as a reference but also as a springboard for experimentation and innovation. Highlights include the following:Implementing quantum neural networks on near-term quantum hardwareQuantum variational optimization for machine learningQuantum-accelerated neural imputations with large language modelsEmerging trends, addressing hardware limitations, algorithm optimization, and ethical considerationsThis book serves as both a primer and an advanced guide by providing essential knowledge for understanding and implementing quantum-enhanced AI solutions in various professional contexts. It equips readers to become active participants in the quantum revolution transforming machine learning. The book explores quantum computing's transformative impact on artificial intelligence and machine learning. Beyond theoretical knowledge, the book emphasizes practical implementation and offers code samples and real-world case studies. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2026
ISBN 10: 1041144652 ISBN 13: 9781041144656
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Añadir al carritoPaperback. Condición: new. Paperback. In the exploration of new frontiers in data-driven solutions, the potential of quantum-enhanced machine learning has become too important to overlook. Quantum machine learning, though still in its formative stages, holds the promise to tackle some of the most complex problems that lie beyond the reach of classical computing. Quantum Machine Learning: Concepts, Algorithms, and Applications is a guide to understanding such quantum principles as superposition and entanglement and how they can enhance learning algorithms and data-processing capabilities. The book features a carefully structured progression from foundational concepts and core algorithms to application-driven case studies and emerging directions for future exploration.The book provides a broad and in-depth treatment of topics ranging from quantum data encoding and quantum neural networks to hybrid models and optimization frameworks. Emphasis has also been placed on real-world use cases and the practical tools available for implementation, thereby ensuring that this book serves not only as a reference but also as a springboard for experimentation and innovation. Highlights include the following:Implementing quantum neural networks on near-term quantum hardwareQuantum variational optimization for machine learningQuantum-accelerated neural imputations with large language modelsEmerging trends, addressing hardware limitations, algorithm optimization, and ethical considerationsThis book serves as both a primer and an advanced guide by providing essential knowledge for understanding and implementing quantum-enhanced AI solutions in various professional contexts. It equips readers to become active participants in the quantum revolution transforming machine learning. The book explores quantum computing's transformative impact on artificial intelligence and machine learning. Beyond theoretical knowledge, the book emphasizes practical implementation and offers code samples and real-world case studies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr. Syed Nisar Hussain Bukhari is an accomplished academician and researcher, currently serving as Scientist D at the National Institute of Electronics and Information Technology (NIELIT), Srinagar, an institute under the Ministry of Electronics a.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2026
ISBN 10: 1041144652 ISBN 13: 9781041144656
Librería: AussieBookSeller, Truganina, VIC, Australia
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Añadir al carritoPaperback. Condición: new. Paperback. In the exploration of new frontiers in data-driven solutions, the potential of quantum-enhanced machine learning has become too important to overlook. Quantum machine learning, though still in its formative stages, holds the promise to tackle some of the most complex problems that lie beyond the reach of classical computing. Quantum Machine Learning: Concepts, Algorithms, and Applications is a guide to understanding such quantum principles as superposition and entanglement and how they can enhance learning algorithms and data-processing capabilities. The book features a carefully structured progression from foundational concepts and core algorithms to application-driven case studies and emerging directions for future exploration.The book provides a broad and in-depth treatment of topics ranging from quantum data encoding and quantum neural networks to hybrid models and optimization frameworks. Emphasis has also been placed on real-world use cases and the practical tools available for implementation, thereby ensuring that this book serves not only as a reference but also as a springboard for experimentation and innovation. Highlights include the following:Implementing quantum neural networks on near-term quantum hardwareQuantum variational optimization for machine learningQuantum-accelerated neural imputations with large language modelsEmerging trends, addressing hardware limitations, algorithm optimization, and ethical considerationsThis book serves as both a primer and an advanced guide by providing essential knowledge for understanding and implementing quantum-enhanced AI solutions in various professional contexts. It equips readers to become active participants in the quantum revolution transforming machine learning. The book explores quantum computing's transformative impact on artificial intelligence and machine learning. Beyond theoretical knowledge, the book emphasizes practical implementation and offers code samples and real-world case studies. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Librería: preigu, Osnabrück, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Quantum Machine Learning | Concepts, Algorithms, and Applications | Syed Nisar Hussain Bukhari | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | Auerbach Publications | EAN 9781041144656 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 151,80
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book explores quantum computing's transformative impact on artificial intelligence and machine learning. Beyond theoretical knowledge, the book emphasizes practical implementation and offers code samples and real-world case studies.