# State of machine learning in Julia

**URL:** <https://discourse.julialang.org/t/state-of-machine-learning-in-julia/74385>\
**Category:** Machine Learning\
**Created:** [January 11, 2022, 10:39am UTC](https://discourse.julialang.org/t/state-of-machine-learning-in-julia/74385 "2022-01-11T10:39:27Z")\
**Posts on this page:** 1\
**Showing post:** 39

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**Author:** ![jzr](https://avatars.discourse-cdn.com/v4/letter/j/eb9ed0/32.png) [@jzr](https://discourse.julialang.org/u/jzr)\
**Post date:** [January 13, 2022, 9:58pm UTC](https://discourse.julialang.org/t/state-of-machine-learning-in-julia/74385/39 "2022-01-13T21:58:18Z")

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I found a similar thread from 2019:

> [@State of deep learning in Julia](https://discourse.julialang.org/t/state-of-deep-learning-in-julia/28049):
>
> Although Julia is promoted as an excellent language for deep learning, I still don’t see any framework I could use in production or even in long-term research. Here are the options I considered in different periods of time: MXNet.jl MXNet.jl is a Julia interface to the core library written in C++ (and Python?). As any wrapper, MXNet uses borrowed data structures and doesn’t feel “native”, which in practise usually means that the library doen’t work well with other common libraries. But the big…

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