krust VS neuronika

I’m not a bioinformatician, I’m just a software engineer. I made krust because I wanted to learn Rust. I’ve kept working on it and I’m surprised it has as many as 22 stars on GitHub, from people who seem to be in bioinformatics for the most part. So it seems like it’s somewhat interesting/useful. But I don’t know what would make this more/less useful for a specialist.

Been learning Rust by making a k-mer counter — bioinformatics 101 tool for counting the frequency of substrings of length k in DNA data: https://github.com/suchapalaver/krust. Would love feedback.

Adding all suggestions as issues on github ;) https://github.com/suchapalaver/krust/issues/6

Thanks again for your suggestion. In case you’re interested, I followed up and used rayon in my program: https://github.com/suchapalaver/krust/blob/main/src/lib.rs. All the best!

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.