swiki
neuronika
swiki | neuronika | |
---|---|---|
1 | 19 | |
8 | 1,033 | |
- | 1.3% | |
1.8 | 0.0 | |
over 2 years ago | over 1 year ago | |
Rust | Rust | |
MIT License | Apache License 2.0 |
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swiki
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What are you using Rust for?
Here's my main Rocket project (uses sqlx + postgres for data storage): https://github.com/Follpvosten/swiki
neuronika
- This year I tried solving AoC using Rust, here are my impressions coming from Python!
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Deep Learning in Rust: Burn 0.4.0 released and plans for 2023
Also perhaps comparing to Neuronika.
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Making a better Tensorflow thanks to strong typing
how does it compare with https://github.com/spearow/juice, https://github.com/neuronika/neuronika and https://github.com/spearow/juice?
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[D] To what extent can Rust be used for Machine Learning?
Check where and how this struct is used. https://github.com/neuronika/neuronika/blob/variable-rework/neuronika-variable/src/history.rs
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What do I need for an ML/DL based scripting language in Rust?
Also you can take a look at neuronika.
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ML in Rust
There is also https://github.com/neuronika/neuronika
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Enzyme: Towards state-of-the-art AutoDiff in Rust
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.
- Any role that Rust could have in the Data world (Big Data, Data Science, Machine learning, etc.)?
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What sort of mature, open-source libraries do you feel Rust should have but currently lacks?
If you like autograd you will love neuronika
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bhtsne 0.5.0, now 5.6x faster on a 4 core machine, plus a summary of my Rust journey (so far)
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.
What are some alternatives?
skytable - Skytable is a modern scalable NoSQL database with BlueQL, designed for performance, scalability and flexibility. Skytable gives you spaces, models, data types, complex collections and more to build powerful experiences
rust-ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
nvim-matrix-bot - Just a bot for Neovim's Matrix room(s)
clblast-rs - clblast bindings for rust
shiba - Display a random Shiba from your terminal whenever you feel the need to. Because why not?
autograph - Machine Learning Library for Rust
krust - counts k-mers, written in rust
are-we-learning-yet - How ready is Rust for Machine Learning?
justrunmydebugger - just run my debugger. see package here: https://build.opensuse.org/package/show/home:ila.embsys:justrunmydebugger/justrunmydebugger
image-shrinker-lite - Drag-and-drop image compression app.
tractjs - Run ONNX and TensorFlow inference in the browser.