EconPapers    
Economics at your fingertips  
 

Convolutional Neural Networks

Charu Aggarwal
Additional contact information
Charu Aggarwal: International Business Machines, IBM T. J. Watson Research Center

Chapter Chapter 9 in Neural Networks and Deep Learning, 2023, pp 305-360 from Springer

Abstract: Abstract Convolutional neural networks are designed to work with grid-structured inputs, which have strong spatial dependencies in local regions of the grid. The most obvious example of grid-structured data is a 2-dimensional image.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-29642-0_9

Ordering information: This item can be ordered from
http://www.springer.com/9783031296420

DOI: 10.1007/978-3-031-29642-0_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2026-05-22
Handle: RePEc:spr:sprchp:978-3-031-29642-0_9