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Repositório Comum
Book . 2024
Data sources: Repositório Comum
ZENODO
Other literature type . 2024
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2024
License: CC BY
Data sources: Datacite
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¿Es Posible Saber Deep Learning Sin Ser Ingeniero? Fundamentos y Reflexiones sobre Modelos Generativos.

descriptionPublicationkeyboard_double_arrow_right Other literature type , Book 01 Jan 2024 Spanish Publisher:Zenodo
Authors: Guerrero-Tamayo, Ana; Sadio-Ramos, Fernando José;

doi: 10.5281/zenodo.12518081 , 10.5281/zenodo.12518080

handle: 10400.26/51239

¿Es Posible Saber Deep Learning Sin Ser Ingeniero? Fundamentos y Reflexiones sobre Modelos Generativos.

Abstract

Guerrero Tamayo, Ana; Sadio-Ramos, Fernando José (2024). ¿Es Posible Saber Deep Learning Sin Ser Ingeniero? Fundamentos y Reflexiones sobre Modelos Generativos. Coimbra: Fernando Ramos (Editor)®. ISBN: 978-989-8486-52-3 DOI: http://dx.doi.org/10.5281/zenodo.12518081

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Keywords

Machine Learning, Law issues on AI, Deep Learning, Ethical issues on AI, Artificial Inteligence

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green