Articles liés à Deep Learning Models: A Practical Approach for Hands-on...
Synopsis
This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With deep learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on deep learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when deep learning is employed. By consolidating all necessary information into a single resource, readers can bypass the hassle of scouring scattered online sources, gaining a one-stop solution to dive into deep learning for object detection and classification. To facilitate understanding, the book employs a rich array of illustrations, figures, tables, and code snippets. Comprehensive code examples are provided, empowering readers to grasp concepts quickly and develop practical solutions. The book covers essential methods and tools, ensuring a complete and comprehensive coverage that enables professionals to implement deep learning algorithms swiftly and effectively.
This book is designed to equip professionals with the necessary skills to thrive in the active field of deep learning, where it has the potential to revolutionize traditional problem-solving approaches. This book serves as a practical companion, enabling readers to grasp concepts swiftly and embark on building practical solutions.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Dr. Jonah Gamba received a B.Sc. degree in Electrical and Electronics Engineering from the University of Zimbabwe in 1994 and an M.Sc. in Computer Science and Engineering from Zhejiang University, Hangzhou, China, in 2000. He obtained a Ph.D. degree in Mathematical Information Systems from Saitama University in 2005. He was a postdoctoral research fellow in information theory at Tsukuba University from 2006 to 2008.
He has been actively involved in research and development activities after obtaining his Ph.D. He conducted research in automotive applications of millimeter-wave radar for advanced vehicle safety and comfort systems. At Tsukuba University's Tsukuba Advanced Research Alliance, he conducted research in video and image processing algorithms as a JST CREST Project postdoctoral researcher.
Dr. Gamba has authored and co-authored several publications in signal processing and related fields. As an inventor, he has registered multiple patents in signal processing applications. His current interests are signal processing for automotive applications, functional safety management, and remote sensing. He has been a visiting researcher of Seikei University and Tokyo University. He is a member of IEEE Signal Processing Society.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 141,30
EUR 9,70 expédition depuis Allemagne vers France
Destinations, frais et délais
Résultats de recherche pour Deep Learning Models: A Practical Approach for Hands-on...
Image d'archives
Deep Learning Models : A Practical Approach for Hands-on Professionals
Edité par
Springer, 2024
ISBN 10 : 9819996716
ISBN 13 : 9789819996711
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 47499925
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Deep Learning Models
Edité par
Springer, Berlin|Springer Nature Singapore|Springer, 2024
ISBN 10 : 9819996716
ISBN 13 : 9789819996711
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With deep learning emerging as a vibrant field of research and development in numerous industrial applications, there is a press. N° de réf. du vendeur 1276622514
Quantité disponible : Plus de 20 disponibles
Image d'archives
Deep Learning Models: A Practical Approach for Hands-On Professionals (Transactions on Computer Systems and Networks)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9789819996711_new
Quantité disponible : Plus de 20 disponibles
Image d'archives
Deep Learning Models : A Practical Approach for Hands-on Professionals
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 47499925-n
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Deep Learning Models
Edité par
Springer Nature Singapore Apr 2024, 2024
ISBN 10 : 9819996716
ISBN 13 : 9789819996711
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With deep learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on deep learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when deep learning is employed. By consolidating all necessary information into a single resource, readers can bypass the hassle of scouring scattered online sources, gaining a one-stop solution to dive into deep learning for object detection and classification. To facilitate understanding, the book employs a rich array of illustrations, figures, tables, and code snippets. Comprehensive code examples are provided, empowering readers to grasp concepts quickly and develop practical solutions. The book covers essential methods and tools, ensuring a complete and comprehensive coverage that enables professionals to implement deep learning algorithms swiftly and effectively.This book is designed to equip professionals with the necessary skills to thrive in the active field of deep learning, where it has the potential to revolutionize traditional problem-solving approaches. This book serves as a practical companion, enabling readers to grasp concepts swiftly and embark on building practical solutions. 216 pp. Englisch. N° de réf. du vendeur 9789819996711
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Deep Learning Models : A Practical Approach for Hands-On Professionals
Edité par
Springer Nature Singapore, Springer Nature Singapore Apr 2024, 2024
ISBN 10 : 9819996716
ISBN 13 : 9789819996711
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware - This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With deep learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on deep learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when deep learning is employed. By consolidating all necessary information into a single resource, readers can bypass the hassle of scouring scattered online sources, gaining a one-stop solution to dive into deep learning for object detection and classification. To facilitate understanding, the book employs a rich array of illustrations, figures, tables, and code snippets. Comprehensive code examples are provided, empowering readers to grasp concepts quickly and develop practical solutions. The book covers essential methods and tools, ensuring a complete and comprehensive coverage that enables professionals to implement deep learning algorithms swiftly and effectively.This book is designed to equip professionals with the necessary skills to thrive in the active field of deep learning, where it has the potential to revolutionize traditional problem-solving approaches. This book serves as a practical companion, enabling readers to grasp concepts swiftly and embark on building practical solutions. N° de réf. du vendeur 9789819996711
Quantité disponible : 1 disponible(s)
Image d'archives
Deep Learning Models : A Practical Approach for Hands-on Professionals
Edité par
Springer, 2024
ISBN 10 : 9819996716
ISBN 13 : 9789819996711
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 47499925
Quantité disponible : Plus de 20 disponibles
Image d'archives
Deep Learning Models : A Practical Approach for Hands-on Professionals
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 47499925-n
Quantité disponible : Plus de 20 disponibles
Image d'archives
Deep Learning Models: A Practical Approach for Hands-On Professionals (Transactions on Computer Systems and Networks)
Vendeur : Books Puddle, New York, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. 2024th edition NO-PA16APR2015-KAP. N° de réf. du vendeur 26399407429
Quantité disponible : 1 disponible(s)
Image d'archives
Deep Learning Models (Hardcover)
Edité par
Springer Verlag, Singapore, Singapore, 2024
ISBN 10 : 9819996716
ISBN 13 : 9789819996711
Neuf
Couverture rigide
Edition originale
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : new. Hardcover. This book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With deep learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on deep learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when deep learning is employed. By consolidating all necessary information into a single resource, readers can bypass the hassle of scouring scattered online sources, gaining a one-stop solution to dive into deep learning for object detection and classification. To facilitate understanding, the book employs a rich array of illustrations, figures, tables, and code snippets. Comprehensive code examples are provided, empowering readers to grasp concepts quickly and develop practical solutions. The book covers essential methods and tools, ensuring a complete and comprehensive coverage that enables professionals to implement deep learning algorithms swiftly and effectively.This book is designed to equip professionals with the necessary skills to thrive in the active field of deep learning, where it has the potential to revolutionize traditional problem-solving approaches. This book serves as a practical companion, enabling readers to grasp concepts swiftly and embark on building practical solutions. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9789819996711
Quantité disponible : 1 disponible(s)