viska VS neuronika

rsip is part of my Rust Voip stack, along with rsip-dns that I have recently been building through viska, a SIP server. SIP protocol is quite tricky to implement, because it's completely P2P so it has some interesting edge cases compared to HTTP which is way more simple. Viska is wip, needed a good SIP library to be productive there, but now that rsip is kinda done, I am gona move back to it :)

I noticed this: https://github.com/vasilakisfil/viska I have not looked at it at all :-) It seems to have been updated fairly recently. Looking at the Readme, there is still a fair way to go, but they've made pretty good progress.

i already found someone with a github repo building a SIP server here: https://github.com/vasilakisfil/viska

That said, there's this, which looks to be on the road already! https://github.com/vasilakisfil/viska

Also perhaps comparing to Neuronika.

how does it compare with https://github.com/spearow/juice, https://github.com/neuronika/neuronika and https://github.com/spearow/juice?

Check where and how this struct is used. https://github.com/neuronika/neuronika/blob/variable-rework/neuronika-variable/src/history.rs

Also you can take a look at neuronika.

There is also https://github.com/neuronika/neuronika

I have a question: as the maintainer of [neuronika](https://github.com/neuronika/neuronika), a crate that offers dynamic neural network and auto-differentiation with dynamic graphs, I'm looking at a future possible feature for such framework consisting in the possibility of compiling models, getting thus rid of the "dynamic" part, which is not always needed. This would speed the inference and training times quite a bit.

If you like autograd you will love neuronika

After reading most of the book, I wanted to get my hands dirty. My initial idea was to build a small machine learning framework but I deemed it to be too difficult if not impossible for me at the time. (Now, neuronika would have something to say). When gathering the bibliography for my thesis, I recalled to have stumbled upon a particular algorithm, t-SNE, whom I liked very much. I found the idea behind it to be very clever and elegant (t-SNE it's still one of my favorite algorithms, together with backprop and SOM, I find manifold learning fascinating in general). "So be it", I said, and I began writing a mess of a code, that was basically a translation of the C++ implementation. Boy was it bad.