22000x faster algos, 50% less memory usage, works on all hardware  new and old.
If you want to collab on fast algorithms  msg me!!
Join our Discord server on making AI faster, or if you just wanna chat about AI!! https://discord.gg/k8AtkZqNwr
What's going to be in Hyperlearn 2022!
! Hyperlearn is under construction! A brand new stable package will be uploaded sometime in 2022! Stay tuned!
Moonshot Website
Documentation
50 Page Modern Big Data Algorithms PDF
In 20182020, I was at NVIDIA helping make GPU ML algos faster! I incorporated Hyperlearn's methods to make TSNE 2000x faster, and others faster. Since then, I have 50+ fast algos, but didn't have time to update Hyperlearn since Moonshot was priority one! I'll be updating Hyperlearn late 2022!
Hyperlearn's algorithms, methods and repo has been featured or mentioned in 5 research papers!
+ Microsoft, UW, UC Berkeley, Greece, NVIDIA
Hyperlearn's methods and algorithms have been incorporated into more than 6 organizations and repositories!
+ NASA + Facebook's Pytorch, Scipy, Cupy, NVIDIA, UNSW
During Hyperlearn's development, bugs and issues were notified to GCC!
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:
 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
Around mid 2022, Hyperlearn will evolve to GreenAI and aims to incorporate:
 New Paratrooper optimizer  fastest SGD variant combining Lookahead, Learning Rate Range Finder, and more!
 30% faster Matrix Multiplication on CPUs
 Software Support for brain floating point (bfloat16) on nearly all hardware
 Easy compilation on old and new CPU hardware (x86, ARM)
 100x faster regular expressions
 50% faster and 50% less memory usage for assembly kernel accelerated methods
 Fast and parallelized New York Times scraper
 Fast and parallelized NYSE Announcements scraper
 Fast and parallelized FRED scraper
 Fast and parallelized Yahoo Finance scraper
I also published a mini 50 page book titled "Modern Big Data Algorithm".
Modern Big Data Algorithms PDF
Comparison of Speed / Memory
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
Help is really needed! Message me!
Key Methodologies and Aims
1. Embarrassingly Parallel For Loops
 Including Memory Sharing, Memory Management
 CUDA Parallelism through PyTorch & Numba
2. 50%+ Faster, 50%+ Leaner
3. Why is Statsmodels sometimes unbearably slow?
 Confidence, Prediction Intervals, Hypothesis Tests & Goodness of Fit tests for linear models are optimized.
 Using Einstein Notation & Hadamard Products where possible.
 Computing only what is neccessary to compute (Diagonal of matrix and not entire matrix).
 Fixing the flaws of Statsmodels on notation, speed, memory issues and storage of variables.
4. Deep Learning Drop In Modules with PyTorch
 Using PyTorch to create ScikitLearn like drop in replacements.
5. 20%+ Less Code, Cleaner Clearer Code
 Using Decorators & Functions where possible.
 Intuitive Middle Level Function names like (isTensor, isIterable).
 Handles Parallelism easily through hyperlearn.multiprocessing
6. Accessing Old and Exciting New Algorithms
 Matrix Completion algorithms  Non Negative Least Squares, NNMF
 Batch Similarity Latent Dirichelt Allocation (BSLDA)
 Correlation Regression
 Feasible Generalized Least Squares FGLS
 Outlier Tolerant Regression
 Multidimensional Spline Regression
 Generalized MICE (any model drop in replacement)
 Using Uber's Pyro for Bayesian Deep Learning
Goals & Development Schedule
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!
Join Moonshot!
Extra License Terms
 The Apache 2.0 license is adopted.