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Springer Handbook of Deep Learning Applications - Springer 136

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Description

This comprehensive handbook explores deep learning applications across diverse fields including computer vision, natural language processing, gene expression analysis, object recognition, autonomous vehicles, semantic image segmentation, brain-computer interfaces, and big data processing. The book covers advanced machine learning techniques that combine multiple layers of nonlinear units, hierarchical deep learning networks for game-playing applications using regret matching, and methods for building GPU-accelerated deep learning frameworks. Ideal for researchers, practitioners, and students seeking practical insights into deploying deep learning solutions across industry and research domains.

Key Features

Covers deep learning applications in computer vision, natural language processing, and autonomous vehicle technology.

Explores advanced topics including brain-computer interfaces and semantic image segmentation techniques.

Includes guidance on building GPU-accelerated deep learning frameworks for high-performance computing.

Presents hierarchical deep learning networks designed for complex game-playing applications.

Addresses big data processing methods using deep learning algorithms and nonlinear units.

Part of Springer's Smart Innovation, Systems and Technologies series (volume 136) for academic credibility.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
06 March 2019
Listed Since
11 December 2018

Barcode

No barcode data available

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