£125.13

Springer Advances in Deep Learning: 57 - Studies in Big Data

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Description

Explore the latest developments in neural networks with Advances in Deep Learning: 57. Part of the Studies in Big Data series by Springer, this book provides a comprehensive look at both basic and advanced concepts in deep network models. It is designed to help readers understand the state-of-the-art architectures used to overcome the limitations of traditional artificial neural networks. This text goes beyond theory by discussing various deep architecture models and their specific components in detail. Each model is illustrated through practical algorithms and selected applications to ensure a clear understanding of how these systems function. A major highlight of this volume is the detailed explanation of the transfer learning approach. You will learn how to use this method to achieve faster training of deep models. The book also demonstrates these techniques using large volumes of fingerprint and face image datasets, making it a practical resource for anyone studying modern AI and machine learning architectures.

Key Features

Covers both basic and advanced concepts in deep network models to build a strong foundation in neural architectures.

Explains state-of-the-art deep architectures designed to address the limitations found in traditional artificial neural networks.

Provides detailed discussions on various deep architecture models and their specific components for technical clarity.

Includes practical illustrations of models through the use of algorithms and selected real-world applications.

Details the transfer learning approach to help achieve faster training times for complex deep models.

Demonstrates practical applications using large volumes of fingerprint and face image datasets.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
25 March 2019
Listed Since
24 January 2019

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