Deep Learning: Concepts and Architectures
Deep Learning: Concepts and Architectures
edited by Witold Pedrycz, Shyi-Ming Chen
- First edition
- 1 recurso en línea (XII, 342 páginas) 135 ilustraciones, 95 ilustraciones a color
- Studies in Computational Intelligence 866 1860-949X Intelligent Technologies and Robotics (Springer-42732) .
Preface -- Chapter 1. Deep Learning Architectures -- Chapter 2. Theoretical Characterization of Deep Neural Networks -- Chapter 3. Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models, etc.
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.
9783030317560
10.1007/978-3-030-31756-0 doi
Aprendizaje automático
Sistemas autoorganizativos
Q325.5 / 2020 EB
Preface -- Chapter 1. Deep Learning Architectures -- Chapter 2. Theoretical Characterization of Deep Neural Networks -- Chapter 3. Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models, etc.
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.
9783030317560
10.1007/978-3-030-31756-0 doi
Aprendizaje automático
Sistemas autoorganizativos
Q325.5 / 2020 EB