winsafe VS neuronika

I know all that shit because I'm the author of Rust's WinSafe library, which is a safer Rust layer over native Win32, so I had to deal with a lot of shit like this.

[dependencies] winsafe = { git = "https://github.com/rodrigocfd/winsafe", features = ["kernel"] }

Externally, you can write a lot of native Windows stuff in Rust already. Personally, I'm having a lot of fun with WinSafe.

If you had such problems (which I also had in the past), I'm really interested in you opinion about WinSafe, and if it could help you solving them.

If you're after a native Windows application, WinSafe may suit your needs.

As the author of WinSafe, I can say it's tedious sometimes, but it's often very challenging, because you have to translate crazy unsafe behaviors into Rust's ownership model. It's surely hard, but also very rewarding at the end.

As the author of the WinSafe lib, I wholeheartedly agree.

I don't know how rare this is (or how rare it should be), but this issue warned me about this potential problem, and I had to make a huge refactoring to treat the possibility. I had to rethink many aspects of all handle implementations. It was hard work, but in the end it was worth it.

WinSafe says hello.

WinSafe, for example (which attemps to be a safe layer over the Windows API), provides lots of RAII automations. A fine example is the BeginPaint function:

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.