Accuracy And Predective Power In Deep Neural Networks
Language: English
Published by SHARK NAIL, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 56.82
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Accuracy And Predective Power In Deep Neural Networks by Brad Fiver explores the principles, methods, and practical considerations involved in understanding and evaluating the accuracy and predictive performance of deep neural networks. The book focuses on the relationship between deep learning architectures, model training, data characteristics, optimization techniques, and the reliability of predictions produced by neural network models.Deep neural networks have become fundamental to modern artificial intelligence and machine learning, supporting applications in areas such as pattern recognition, classification, prediction, computer vision, natural language processing, and intelligent decision-making. As these models become increasingly sophisticated, understanding their predictive behavior and evaluating their accuracy are essential for developing effective and dependable AI systems.This book provides a technical perspective on deep neural network performance, emphasizing concepts related to predictive accuracy, model evaluation, training behavior, feature representation, learning processes, and performance assessment. It is intended to help readers develop a stronger understanding of the factors that influence how neural networks learn from data and generate predictions.Accuracy And Predective Power In Deep Neural Networks is suitable for students, researchers, engineers, developers, data scientists, and professionals working with artificial intelligence, machine learning, deep learning, and neural network-based predictive systems. It can also serve as a useful reference for readers seeking a focused introduction to the evaluation and predictive capabilities of modern deep learning models.…
Seller Inventory # 9781962116572
- Title
- Accuracy And Predective Power In Deep Neural Networks
- Author
- Brad Fiver
- Publisher
- SHARK NAIL
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1962116573
- ISBN 13
- 9781962116572
- Item weight
- 189 grams
- Dimensions
- 229x152x7 mm
Accuracy And Predective Power In Deep Neural Networks by Brad Fiver explores the principles, methods, and practical considerations involved in understanding and evaluating the accuracy and predictive performance of deep neural networks. The book focuses on the relationship between deep learning architectures, model training, data characteristics, optimization techniques, and the reliability of predictions produced by neural network models.
Deep neural networks have become fundamental to modern artificial intelligence and machine learning, supporting applications in areas such as pattern recognition, classification, prediction, computer vision, natural language processing, and intelligent decision-making. As these models become increasingly sophisticated, understanding their predictive behavior and evaluating their accuracy are essential for developing effective and dependable AI systems.
This book provides a technical perspective on deep neural network performance, emphasizing concepts related to predictive accuracy, model evaluation, training behavior, feature representation, learning processes, and performance assessment. It is intended to help readers develop a stronger understanding of the factors that influence how neural networks learn from data and generate predictions.
Accuracy And Predective Power In Deep Neural Networks is suitable for students, researchers, engineers, developers, data scientists, and professionals working with artificial intelligence, machine learning, deep learning, and neural network-based predictive systems. It can also serve as a useful reference for readers seeking a focused introduction to the evaluation and predictive capabilities of modern deep learning models.
"Synopsis" may belong to another edition of this title.
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