[ddragon]

If you mean Zygote.jl, it's a very ambitious project (like Swift for Tensorflow which has been under development for even longer I believe) with not many people working on it compared to Tensorflow and pytorch. And Pytorch for example only supports the method it decides to overload, while Zygote aims to support everything in the language (including stuff that isn't as obvious like state, IO, control flow in general). And then you have optimizations over the computation graph, memory management on GPU and many corner cases I can't imagine.

Though you can already use very clean Pytorch style libraries like Flux and Knet or the Tensorflow bindings to leverage the benefits of Julia for high performance numerical processing on the adjacent tasks such as data preprocessing.

[carapace]

"A Differentiable Programming System to Bridge Machine Learning and Scientific Computing"

https://arxiv.org/abs/1907.07587

https://news.ycombinator.com/item?id=20477873

From the abstract:

> We describe Zygote, a Differentiable Programming system that is able to take gradients of general program structures. We implement this system in the Julia programming language. Our system supports almost all language constructs (control flow, recursion, mutation, etc.) and compiles high-performance code without requiring any user intervention or refactoring to stage computations.

Just linking to this for those who haven't seen it.