Overview Machine Learning and Deep Learning Frameworks

Volker Liermann ()
Additional contact information
Volker Liermann: ifb SE

A chapter in The Digital Journey of Banking and Insurance, Volume III, 2021, pp 187-224 from Springer

Abstract: Abstract This chapter provides an overview of the different machine learning (ML) and deep learning (DL) frameworks, aiming to show the variety ranging from different open-source initiatives through to standard software vendors and specialized start-ups contributing to the enormous amount of tools to analyze, condense and predict data.

Keywords: Machine learning frameworks; Deep learning framework (search for similar items in EconPapers)
Date: 2021
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:978-3-030-78821-6_12

Ordering information: This item can be ordered from
http://www.springer.com/9783030788216

DOI: 10.1007/978-3-030-78821-6_12

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 ().