Deep Learning Framework Analysis

Collins Achepsah Leke, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

In this chapter, we investigate the effectiveness of using a deep autoencoder network with three and five hidden layers. These networks will be used in combination with optimization algorithms to perform missing data estimation tasks. The results from these networks will be compared against those obtained from using the seven hidden-layered deep autoencoder network from the literature. The network training times are observed to increase with the increasing number of hidden layers.

Original languageEnglish
Title of host publicationStudies in Big Data
PublisherSpringer Science and Business Media Deutschland GmbH
Pages147-171
Number of pages25
DOIs
Publication statusPublished - 2019

Publication series

NameStudies in Big Data
Volume48
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Deep Learning Framework Analysis'. Together they form a unique fingerprint.

Cite this