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Deep Learning: Concepts and Architectures

Overview of attention for book
Cover of 'Deep Learning: Concepts and Architectures'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Deep Learning Architectures
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    Chapter 2 Theoretical Characterization of Deep Neural Networks
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    Chapter 3 Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models
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    Chapter 4 Assessment of Autoencoder Architectures for Data Representation
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    Chapter 5 The Encoder-Decoder Framework and Its Applications
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    Chapter 6 Deep Learning for Learning Graph Representations
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    Chapter 7 Deep Neural Networks for Corrupted Labels
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    Chapter 8 Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task
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    Chapter 9 Using Convolutional Neural Networks to Forecast Sporting Event Results
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    Chapter 10 Heterogeneous Computing System for Deep Learning
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    Chapter 11 Progress in Neural Network Based Statistical Language Modeling
Attention for Chapter 5: The Encoder-Decoder Framework and Its Applications
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Citations

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21 Dimensions

Readers on

mendeley
16 Mendeley
Summary Dimensions citations
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Chapter title
The Encoder-Decoder Framework and Its Applications
Chapter number 5
Book title
Deep Learning: Concepts and Architectures
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-31756-0_5
Book ISBNs
978-3-03-031755-3, 978-3-03-031756-0
Authors

Ahmad Asadi, Reza Safabakhsh, Asadi, Ahmad, Safabakhsh, Reza

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 19%
Student > Bachelor 2 13%
Professor 1 6%
Lecturer 1 6%
Student > Doctoral Student 1 6%
Other 0 0%
Unknown 8 50%
Readers by discipline Count As %
Computer Science 5 31%
Social Sciences 1 6%
Medicine and Dentistry 1 6%
Engineering 1 6%
Unknown 8 50%

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