Julia and Flux: Modernizing Machine Learning

Authors

  • Dhairya Gandhi Julia Computing
  • Mike Innes Julia Computing
  • Elliot Saba Julia Computing
  • Keno Fischer Julia Computing
  • Viral Shah Julia Computing

DOI:

https://doi.org/10.5753/compbr.2019.39.4527

Keywords:

Machine Learning, Artificial Intelligence, Performance

Abstract

This paper presents two cutting-edge technologies that facilitate the development of Machine Learning applications without sacrificing execution performance.

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References

Mike Innes, Stefan Karpinski, Viral Shah, David Barber, Pontus Stene- torp, Tim Besard, James Bradbury, Valentin Churavy, Simon Danisch, Alan Edelman, et al. On Machine Learning and Programming Languages, 2018. URL [link]

Mike Innes. What is Differentiable Programming?, 2019. URL [link]

Mike Innes, James Bradbury, Keno Fischer, Dhairya Gandhi, Neethu Mariya Joy, Tejan Karmali, Matt Kelly, Avik Pal, Marco Rudilosso, Saba Elliot, Viral Shah, and Deniz Yuret. Building a Language and Compiler for Machine Learning, 2018. URL [link].

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Published

2019-04-01

How to Cite

Gandhi, D., Innes, M., Saba, E., Fischer, K., & Shah, V. (2019). Julia and Flux: Modernizing Machine Learning. Brazil Computing, (39), 41–45. https://doi.org/10.5753/compbr.2019.39.4527

Issue

No. 39 (2019)

Section

Papers

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.