Name: Signet-CNN: A Deep Learning Model for Intelligent Data Classification: A Deep Learning Model for Intelligent Data Classification (en Inglés)
SKU: 68935818
Price: 121.92 PEN
Availability: InStock
Author: Karol
ISBN: 9798869057273

portada Signet-CNN: A Deep Learning Model for Intelligent Data Classification: A Deep Learning Model for Intelligent Data Classification (en Inglés)

Signet-CNN: A Deep Learning Model for Intelligent Data Classification: A Deep Learning Model for Intelligent Data Classification (en Inglés)

Formato

Libro Físico

Editorial

Beakers Bay

Autor

Karol

Idioma

Inglés

N° páginas

132

Encuadernación

Tapa Blanda

ISBN13

9798869057273

Signet-CNN: A Deep Learning Model for Intelligent Data Classification: A Deep Learning Model for Intelligent Data Classification (en Inglés)

Karol (Autor) · Beakers Bay · Tapa Blanda

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Reseña del libro "Signet-CNN: A Deep Learning Model for Intelligent Data Classification: A Deep Learning Model for Intelligent Data Classification (en Inglés)"

Signet-CNN: A Deep Learning Model for Intelligent Data Classification presents a focused technical treatment of deep learning for automated data classification and intelligent pattern analysis. The book centers on convolutional neural network (CNN) methods and their role in learning meaningful representations from structured or transformed data for classification tasks. It introduces the relationship between deep learning architectures, feature extraction, representation learning, and classification, providing readers with a foundation for understanding how neural models can process complex data and distinguish between relevant categories. The subject connects artificial intelligence, machine learning, deep neural networks, convolutional neural networks, data classification, pattern recognition, feature learning, and computational intelligence. Particular attention is given to the conceptual role of CNN layers in extracting increasingly informative features and supporting classification through learned representations. The book is relevant to readers interested in intelligent data processing, automated classification, machine learning models, and neural network-based analytical methods. It can serve as a useful technical reference for postgraduate learners, researchers, computer science professionals, data scientists, and practitioners seeking to understand deep learning approaches to classification problems. By emphasizing the connection between model architecture and classification, the book provides a structured perspective on how deep neural networks can transform input data into discriminative representations. It also supports broader understanding of the computational principles behind intelligent classification systems, including feature extraction, hierarchical representation, model training, prediction, and evaluation. The material is positioned within the wider field of artificial intelligence and offers terminology and concepts relevant to contemporary deep learning and data analytics.