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Book details
File Size: 12.89 MB
Format: pdf
Print Length: 342 pages
Publisher: Springer; 1st ed. 2020 edition (October 29, 2019)
Publication Date: October 29, 2019
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07ZS2K41L
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.
Table of contents (11 chapters)
Deep Learning Architectures
Theoretical Characterization of Deep Neural Networks
Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models
Assessment of Autoencoder Architectures for Data Representation
The Encoder-Decoder Framework and Its Applications
Deep Learning for Learning Graph Representations
Deep Neural Networks for Corrupted Labels
Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task
Using Convolutional Neural Networks to Forecast Sporting Event Results
Heterogeneous Computing System for Deep Learning
Progress in Neural Network Based Statistical Language Modeling
Book details
File Size: 12.89 MB
Format: pdf
Print Length: 342 pages
Publisher: Springer; 1st ed. 2020 edition (October 29, 2019)
Publication Date: October 29, 2019
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07ZS2K41L
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.
Table of contents (11 chapters)
Deep Learning Architectures
Theoretical Characterization of Deep Neural Networks
Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models
Assessment of Autoencoder Architectures for Data Representation
The Encoder-Decoder Framework and Its Applications
Deep Learning for Learning Graph Representations
Deep Neural Networks for Corrupted Labels
Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task
Using Convolutional Neural Networks to Forecast Sporting Event Results
Heterogeneous Computing System for Deep Learning
Progress in Neural Network Based Statistical Language Modeling