hyperlearn
HyperLearn provides 2-2000x faster machine learning algorithms with 50% less memory usage, optimized for all hardware.
What is hyperlearn?
HyperLearn is a Python machine learning library that provides significantly faster and more memory-efficient implementations of common algorithms compared to standard libraries like Scikit-Learn. It solves the problem of slow and memory-intensive ML workflows by optimizing matrix operations, enabling parallelism, and reducing computational overhead.
Data scientists, machine learning engineers, and researchers who need to process large datasets quickly and efficiently, especially those working with limited hardware resources or requiring real-time performance.
Developers choose HyperLearn for its dramatic speed improvements (up to 2000x faster) and 50% memory reduction, while maintaining full compatibility with Scikit-Learn's API. Its unique selling point is the combination of mathematical optimizations and hardware-agnostic parallelism.
Overview
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Use Cases
Best For
- Processing large-scale datasets with limited memory
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