neuronika VS burn

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.

You can use libtorch directly via `tch-rs`, and at present I'm porting over to Burn (see https://burn.dev) which appears incredibly promising. My impression is it's in a good place, if of course not close to the ecosystem of Python/C++. At very least I've gotten my nn models training and running without too much difficulty. (I'm moving to Burn for the thread safety - their `Tensor` impl is `Sync` - libtorch doesn't have such a guarantee.)

Burn has Candle as one of its backends, which I understand is also quite popular.

[package] name = "resnet_burn" version = "0.1.0" edition = "2021" [dependencies] burn = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11", features = ["ndarray"] } burn-import = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11" } image = { version = "0.24.7", features = ["png", "jpeg"] }

Great!

For Burn project, we have WebGPU example and I was looking into how we could add automated tests in the browser. Now it seems possible.

Here is the image classification example if you'd like to check out:

https://github.com/tracel-ai/burn/tree/main/examples/image-c...

Full Release Note: https://github.com/tracel-ai/burn/releases/tag/v0.11.0