Knet.jl

I feel I should point out that if you need performance and you're mostly only interested in classic PyTorch or TensorFlow style machine learning (i.e. you don't need Flux.jl's characteristic flexibility), then you should really check out KNet.jl [1]. KNet is generally around the same flexibility and performance as something like PyTorch. It doesn't get nearly as much attention as Flux because it's less novel, but it really should be looked at more. [1] https://github.com/denizyuret/Knet.jl

And also https://github.com/denizyuret/Knet.jl https://github.com/malmaud/TensorFlow.jl

Yes, there are already a couple. The most well known is a 100% Julia neural network library called Flux.jl [1], which aims to become what Swift for Tensorflow wants as well (to make the entire Julia language a fully differentiable language) through Zygote.jl [2], and even without it has already great integration with the ecosystem, for example with the differentiable equations library through DiffEqFlux.jl [3]. Plus the source code is very high level (while being high performance, including easy GPU support), so you can easily see what each component does and implement any extension directly on your code without worrying about performance. There is also another feature complete native library that allows some very concise code, Knet.jl [4], and the Tensorflow bindings [5]. [1] https://github.com/FluxML/Flux.jl [2] https://github.com/FluxM

Julia, despite a pretty small userbase, has some great ML libraries. See for example Knet: https://github.com/denizyuret/Knet.jl

This is totally fair. Even the Julia creators regularly mention "stay with existing tools for now, if you are happy." Julia is an increasingly attractive option for greenfield, high-performance development, but for folks who aren't doing a lot of that, a more stable language and mature ecosystem is obviously important. Julia and many libraries can still require some elbow-grease, and the maturity and coverage varies between fields. However, this is rapidly changing as folks do new research in Julia and build up everything else they need along the way. The current situation is very similar to SciPy in the mid-2000s, though with perhaps a somewhat different mix of backgrounds. Here are a few examples: https://github.com/JuliaOpt https://github.com/jeff-regier/Celeste.jl https://github.com/dlfivefifty https://github.com/JuliaInv http://docs.