£137.25

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Springer Advances in Deep Learning: 57 - AI & Machine Learning Book

Amazon

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Description

Advances in Deep Learning is a comprehensive guide to modern neural network architectures and deep learning methodologies. This Springer publication introduces both fundamental and advanced concepts in deep network models, equipping readers with the knowledge to overcome limitations of traditional artificial neural networks. The book explores state-of-the-art deep architecture models in detail, breaking down their components through clear algorithms and practical applications. A major focus is transfer learning, a powerful technique for accelerating model training on large datasets. The content demonstrates these approaches on substantial fingerprint and face image datasets, providing real-world context for implementing deep learning solutions. Ideal for researchers, practitioners, and students seeking to master contemporary deep learning techniques and architectures.

Key Features

Covers both foundational and advanced concepts in deep network models from basic principles through state-of-the-art architectures.

Explains transfer learning methodology for faster training of deep models, with practical demonstrations on large-scale datasets.

Provides detailed breakdowns of deep architecture components with accompanying algorithms and real-world application examples.

Demonstrates fingerprint and face image processing techniques using deep learning on substantial real-world datasets.

Published by Springer as part of the Studies in Big Data series, volume 57, offering academic-quality machine learning content.

Product Specifications

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

Barcode

No barcode data available

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