Versions

Description

HyperLearn aims to make Machine Learning algorithms run in at least 50% of their original time. Algorithms from Linear Regression to Principal Component Analysis are optimized by using LAPACK, BLAS, and parallelized through Numba.

Some key current achievements of HyperLearn:

70% less time to fit Least Squares / Linear Regression than sklearn + 50% less memory usage 50% less time to fit Non Negative Matrix Factorization than sklearn due to new parallelized algo 40% faster full Euclidean / Cosine distance algorithms 50% less time LSMR iterative least squares New Reconstruction SVD - use SVD to impute missing data! Has .fit AND .transform. Approx 30% better than mean imputation 50% faster Sparse Matrix operations - parallelized RandomizedSVD is now 20 - 30% faster

Repository

https://github.com/danielhanchen/hyperlearn.git

Project Slug

hyperlearn

Last Built

8 months, 3 weeks ago passed

Maintainers

Home Page

https://github.com/danielhanchen/hyperlearn

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Tags

machine-learning, data-science, sklearn, artificial-intelligence, fast-machine-learning

Project Privacy Level

Public

Short URLs

hyperlearn.readthedocs.io
hyperlearn.rtfd.io

Default Version

latest

'latest' Version

master