High-quality, open-source libraries built with precision and passion. From neural networks and Bayesian inference to statistical models, we distill complex algorithms into elegant, usable code.
A complete LSTM neural network library with training capabilities, multiple optimizers, and peephole variants.
Ruby gem with Rasch, 2PL, and 3PL implementations for Item Response Theory (IRT), supporting missing data handling and adaptive optimization.
A Rust library designed to estimate the value of π (pi) using Monte Carlo simulations with high precision and performance.
A Rust library for Bayesian inference with MCMC samplers. Modern statistical analysis with powerful probabilistic programming capabilities.
SyntaxSpirits is dedicated to creating high-quality, open-source libraries that make complex algorithms accessible to developers. Each project is crafted with attention to detail, performance, and usability.
From machine learning implementations and Bayesian inference to statistical modeling tools, we believe in the power of well-documented, thoroughly tested code that empowers others to build amazing things.