Accuracy And Predective Power In Deep Neural Networks

Fiver, Brad

ISBN 10: 1962116573 / ISBN 13: 9781962116572

Published by Shark Nail, 2026

About this Item

Seller Inventory # I-9781962116572

Bibliographic Details

• Title: Accuracy And Predective Power In Deep Neural Networks
• Publisher: Shark Nail
• Publication Date: 2026
• Language: English
• ISBN 10: 1962116573
• ISBN 13: 9781962116572
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.