Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Alemania
EUR 17,00
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Añadir al carritoXVI, 223 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Intelligent Systems Reference Library. Volume 158. Sprache: Englisch.
Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Alemania
EUR 18,00
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Añadir al carritoxvi, 370 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
Librería: StainesBook, Weybridge, SURRE, Reino Unido
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Librería: Buchpark, Trebbin, Alemania
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Añadir al carritoCondición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators¿ and learners¿ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:¿ Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;¿ Using learning analytics to predict student performance;¿ Using learning analytics to create learning materials and educational courses; and¿ Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 159,00
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 160,16
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 178,14
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Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
EUR 181,71
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 181,75
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 182,68
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 194,64
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Idioma: Inglés
Publicado por Springer International Publishing AG, Cham, 2024
ISBN 10: 3031598105 ISBN 13: 9783031598104
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 196,94
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Añadir al carritoHardcover. Condición: new. Hardcover. This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.This book provides numerous ways that deep learners can use for logo recognition, including:Deep learning-based end-to-end trainable architecture for logo detectionWeakly supervised logo recognition approach using attention mechanismsAnchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world imagesUnsupervised logo detection that takes into account domain-shift issues from synthetic to real-world imagesApproach for logo detection modeling domain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem.The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks.The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 180,27
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 194,80
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 183,84
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 200,33
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 201,42
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Añadir al carritoCondición: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 208,34
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 208,45
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Añadir al carritoCondición: New. 2024th edition NO-PA16APR2015-KAP.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 211,83
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Idioma: Inglés
Publicado por Springer International Publishing, 2019
ISBN 10: 3030137422 ISBN 13: 9783030137427
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 160,49
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators' and learners' data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:- Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;- Using learning analytics to predict student performance;- Using learning analytics to create learning materials and educational courses; and- Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.
Idioma: Inglés
Publicado por Springer International Publishing, 2021
ISBN 10: 3030497267 ISBN 13: 9783030497262
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 160,49
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance.This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
Idioma: Inglés
Publicado por Springer International Publishing, 2020
ISBN 10: 3030497232 ISBN 13: 9783030497231
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 160,49
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance.This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.