! Hyperlearn is under construction! A brand new stable package will be uploaded sometime in 2022! Stay tuned!
Documentation
50 Page Modern Big Data Algorithms PDF
+ Microsoft, UW, UC Berkeley, Greece, NVIDIA
+ NASA + Facebook's Pytorch, Scipy, Cupy, NVIDIA, UNSW
HyperLearn is written completely in PyTorch, NoGil Numba, Numpy, Pandas, Scipy & LAPACK, C++, C, Python, Cython and Assembly, and mirrors (mostly) Scikit Learn. HyperLearn also has statistical inference measures embedded, and can be called just like Scikit Learn's syntax.
Some key current achievements of HyperLearn:
Around mid 2022, Hyperlearn will evolve to GreenAI and aims to incorporate:
I also published a mini 50 page book titled "Modern Big Data Algorithm".
Modern Big Data Algorithms PDF
Algorithm | n | p | Time(s) | RAM(mb) | Notes | ||
---|---|---|---|---|---|---|---|
Sklearn | Hyperlearn | Sklearn | Hyperlearn | ||||
QDA (Quad Dis A) | 1000000 | 100 | 54.2 | 22.25 | 2,700 | 1,200 | Now parallelized |
LinearRegression | 1000000 | 100 | 5.81 | 0.381 | 700 | 10 | Guaranteed stable & fast |
Time(s) is Fit + Predict. RAM(mb) = max( RAM(Fit), RAM(Predict) )
I've also added some preliminary results for N = 5000, P = 6000
Hyperlearn will be revamped in the following months to become Moonshot GreenAI with over an extra 150 optimized algorithms! Stay tuned!! Also you made it this far! If you want to join Moonshot, complete the secretive quiz!