# Deep learning in Julia

**URL:** <https://discourse.julialang.org/t/deep-learning-in-julia/112844>\
**Category:** Machine Learning\
**Created:** [April 11, 2024, 7:53pm UTC](https://discourse.julialang.org/t/deep-learning-in-julia/112844 "2024-04-11T19:53:30Z")\
**Posts on this page:** 1\
**Showing post:** 4

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**Author:** ![cpfiffer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cpfiffer/32/208747_2.png) [@cpfiffer](https://discourse.julialang.org/u/cpfiffer)\
**Post date:** [April 11, 2024, 8:18pm UTC](https://discourse.julialang.org/t/deep-learning-in-julia/112844/4 "2024-04-11T20:18:33Z")

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A very thorough analysis of nice-to-haves:

> [@State of machine learning in Julia](https://discourse.julialang.org/t/state-of-machine-learning-in-julia/74385/4):
>
> I’ll offer a perspective from someone who (as a conscious choice) primarily uses Python over Julia. I work with, and maintain libraries for, all of PyTorch, JAX, and Julia. For context my answers will draw some parallels between: JAX, with [Equinox](https://github.com/patrick-kidger/equinox) for neural networks; Julia, with Flux for neural networks; as these actually feel remarkably similar. JAX and Julia are both based around jit-compilers; both ubiquitously perform program transforms via homoiconicity. Equinox and Flux both build mod…

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