<p>Accelerating research with an open source, declarative framework for deep learning experiments</p>

Layne Sadler

doi:10.7490/f1000research.1119044.1

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Sadler L. Accelerating research with an open source, declarative framework for deep learning experiments [version 1; not peer reviewed]. F1000Research 2022, 11:817 (slides) (https://doi.org/10.7490/f1000research.1119044.1)

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Accelerating research with an open source, declarative framework for deep learning experiments

Layne Sadler¹

Published 23 Jul 2022 (https://doi.org/10.7490/f1000research.1119044.1)

Author Affiliations

¹ AIQC, USA

Copyright © 2022 Sadler L. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 23 Jul 2022

Accelerating research with an open source, declarative framework for deep learning experiments
[version 1; not peer reviewed]

Layne Sadler¹

Author Affiliations

¹ AIQC, USA

Presented at

Intelligent Systems for Molecular Biology (ISMB) 2022

Abstract

Competing Interests

No competing interests were disclosed

Keywords

machine learning, ai, deep learning, analytics, genomics, multi omics, cohort

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