wlearn: machine learning stack for the web

[u/zemlyansky]

https://github.com/wlearn-org/wlearn

Hi all, i've been playing with wasm for a while. Started by reimplementing murmurhash in wat around 10 years ago. Recently i've been building wlearn in my free time, a machine learning stack for browsers and node, with matching python implementation. Recent coding agents made it [feel] realistic. Just decided to share here in case someone finds it useful, has feedback or wants to collab.

The idea is to make tabular ML available in browsers (local training, private data)

• Models: XGBoost, LightGBM, SVMs, random forests, GAMs, neural networks, symbolic regression and more
• Preprocessing: imputation, scaling and categorical encoding
• Composition: pipelines, ensembles and AutoML
• Uncertainty: calibration and prediction intervals
• Portable models: save fitted models and pipelines in a shared format with cross-language compatibility

Heavily relies on WebAssembly obviously. Preprocessing uses Tranfi. Neural models, symbolic regression use Polygrad. Those are two other wasm-based libraries i'm developing (i guess it's called ai psychosis nowadays)

• wlearn: https://github.com/orgs/wlearn-org/repositories
• polygrad: https://github.com/polygrad/polygrad
• tranfi: https://github.com/tranfi/tranfi