This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.
Erscheinungsdatum: 13.11.2019

lgrsnf/深度学习概念和架构.pdf

Deep Learning: Concepts and Architectures (Studies in Computational Intelligence Book 866)

Deep learning : convergence to big data analytics

Witold Pedrycz; Shyi-Ming Chen; SpringerLink (Online service)

Murad Khan

Springer International Publishing : Imprint: Springer

Springer Nature Switzerland AG

Studies in computational intelligence, v. 866, Cham, Switzerland, 2020

Studies in computational intelligence, 866, 1st ed. 2020, Cham, 2020

SpringerBriefs in computer science, Singapore, 2019

Springer Nature, Cham, 2019

Switzerland, Switzerland

1st ed. 2020, PT, 2019

Oct 30, 2019

2, 20191029

Source title: Deep Learning: Concepts and Architectures (Studies in Computational Intelligence)

This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions

2024-02-25