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
8 months, 3 weeks ago passed
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