unslothai/hyperlearn

Health v2 · maintenance only

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2505 stars · 167 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

HyperLearn is a Python library providing optimized reimplementations of classical machine learning and statistical algorithms (SVD, eigendecomposition, regression, imputation) that are 2-2000x faster and use ~50% less memory than scikit-learn/NumPy equivalents. It works on both old and new hardware, including CPU and GPU, and its methods have been incorporated into PyTorch, SciPy, CuPy, and NVIDIA RAPIDS.

Use cases

• speed up slow SVD and eigendecomposition in numpy or pytorch
• run scikit-learn style algorithms with less memory on large datasets
• fit ridge regression and linear models faster on CPU
• accelerate classical ML on old hardware without a modern GPU
• fast data imputation for missing values
• speed up t-SNE style embeddings used in RAPIDS

When to choose

• you need faster linear algebra primitives (SVD, EIGH, GELS) than NumPy/PyTorch provide
• you are memory-constrained when running classical ML on big data
• you want scikit-learn-like APIs that work on older CPUs and GPUs

When to avoid

• you need actively maintained, production-supported ML tooling
• you need deep learning model training rather than classical algorithms
• you need the latest scikit-learn features and estimators

Facets

library · maturity maintenance

machine-learning math data-science gpu-computing machine-learning data-science performance python cross-platform scikit-learn numpy linear-algebra svd regression econometrics statistics memory-optimization algorithms gpu

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