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.
Author(s): Witold Pedrycz, Shyi-Ming Chen
Series: Studies In Computational Intelligence vol. 866
Publisher: Springer
Year: 2020
Language: English
Pages: 347
Tags: Computational Intelligence, Deep Learning
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
