Copertina

The State of Julia for Scientific Machine Learning

Salvato in:
Pubblicato in:arXiv.org (Dec 20, 2024), p. n/a
Autore principale: Berman, Edward
Altri autori: Ginesin, Jacob
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Python
Machine learning
Accesso online:Citation/Abstract
Full text outside of ProQuest
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045 0 |b d20241220 
100 1 |a Berman, Edward 
245 1 |a The State of Julia for Scientific Machine Learning 
260 |b Cornell University Library, arXiv.org  |c Dec 20, 2024 
513 |a Working Paper 
520 3 |a Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's inception in 2012 and declaration of language goals in 2017, its ecosystem and language-level features have grown tremendously. In this paper, we take a modern look at Julia's features and ecosystem, assess the current state of the language, and discuss its viability and pitfalls as a replacement for Python as the de-facto scientific machine learning language. We call for the community to address Julia's language-level issues that are preventing further adoption. 
653 |a Python 
653 |a Machine learning 
700 1 |a Ginesin, Jacob 
773 0 |t arXiv.org  |g (Dec 20, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3117168867/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2410.10908