Deep Learning: Concepts And Architectures

Description

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. Front Matter ....Pages i-xii Deep Learning Architectures (Mohammad-Parsa Hosseini, Senbao Lu, Kavin Kamaraj, Alexander Slowikowski, Haygreev C. Venkatesh)....Pages 1-24 Theoretical Characterization of Deep Neural Networks (Piyush Kaul, Brejesh Lall)....Pages 25-63 Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models (Yuri Gordienko, Yuriy Kochura, Vlad Taran, Nikita Gordienko, Alexandr Rokovyi, Oleg Alienin et al.)....Pages 65-99 Assessment of Autoencoder Architectures for Data Representation (Karishma Pawar, Vahida Z. Attar)....Pages 101-132 The Encoder-Decoder Framework and Its Applications (Ahmad Asadi, Reza Safabakhsh)....Pages 133-167 Deep Learning for Learning Graph Representations (Wenwu Zhu, Xin Wang, Peng Cui)....Pages 169-210 Deep Neural Networks for Corrupted Labels (Ishan Jindal, Matthew Nokleby, Daniel Pressel, Xuewen Chen, Harpreet Singh)....Pages 211-235 Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task (Yalong Jiang, Zheru Chi)....Pages 237-268 Using Convolutional Neural Networks to Forecast Sporting Event Results (Mu-Yen Chen, Ting-Hsuan Chen, Shu-Hong Lin)....Pages 269-285 Heterogeneous Computing System for Deep Learning (Mihaela Maliţa, George Vlǎduţ Popescu, Gheorghe M. Ştefan)....Pages 287-319 Progress in Neural Network Based Statistical Language Modeling (Anup Shrikant Kunte, Vahida Z. Attar)....Pages 321-339 Back Matter ....Pages 341-342