Erdős-Rényi and small world ensembles. For simple sampling, Markov chains etc.
Machine learning framework with spatial modeling, conformal prediction, and gradient boosting competitive with C++
A Machine Learning framework for Rust
Python bindings for sklears machine learning library using PyO3
High-performance SIMD backend for SC-NeuroCore stochastic neuromorphic computing
Pingora-based reverse proxy with ML-powered security
Riak-compatible protocol surface (HTTP + PBC) and storage bridge for the Dynomite Rust port
Complete machine learning framework in Rust - tensors, neural networks, 85+ ML algorithms, GPU acceleration, WASM, FFI, model serving, compiler optimizations, distributed training, and 8 hardware accelerators - all included by default
Neural network layers for GhostFlow ML framework
Classical regression models (OLS, Ridge, Lasso, Elastic Net, GLM, GP) for anofox-ml
Streaming ML in Rust -- gradient boosted trees, neural architectures (TTT/KAN/MoE/Mamba/SNN), AutoML, kernel methods, and composable pipelines
Training loops, loss composition, and optimization schedules for TensorLogic
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