<p align="center"> <img src="Images/HyperLearn_Logo.png" alt="drawing" width="300"/> </p> <h4 align="center"> 2-2000x faster algos, 50% less memory usage, works on all hardware - new and old. </h4> <h2 align="center"> 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/unsloth </h2>

<a href="http://unsloth.ai/" style="text-align: center"><p align="center">Unsloth Website</p></a> <a href="https://hyperlearn.readthedocs.io/en/latest/index.html" style="text-align: center"><p align="center">Documentation</p></a> <a href="https://drive.google.com/file/d/18fxyBiPE0G4e5yixAj5S--YL_pgTh3Vo/view?usp=sharing" style="text-align: center"><p align="center">50 Page Modern Big Data Algorithms PDF</p></a>

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<h3> Hyperlearn's algorithms, methods and repo has been featured or mentioned in 5 research papers! </h3>

+ Microsoft, UW, UC Berkeley, Greece, NVIDIA

• Microsoft: Yu et al. Making Classical Machine Learning Pipelines Differentiable http://learningsys.org/nips18/assets/papers/45CameraReadySubmissionfinetune.pdf
• University of Washington: Ariel Rokem, Kendrick Kay. Fractional ridge regression: a fast, interpretable reparameterization of ridge regression https://arxiv.org/abs/2005.03220
• National Center for Scientific Research 'Demokritos', Greece: Christos Platias, Georgios Petasis. A Comparison of Machine Learning Methods for Data Imputation https://dl.acm.org/doi/10.1145/3411408.3411465
• UC Berkeley David Chan. GPU Accelerated T-Distributed Stochastic Neighbor Embedding https://digitalassets.lib.berkeley.edu/techreports/ucb/incoming/EECS-2020-89.pdf (Incorporated Hyperlearn methods into NVIDIA RAPIDS TSNE)
• NVIDIA: Raschka et al. RAPIDS: Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence https://arxiv.org/abs/2002.04803 (Incorporated Hyperlearn methods into NVIDIA RAPIDS TSNE)

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<h3> Hyperlearn's methods and algorithms have been incorporated into more than 6 organizations and repositories! </h3>

+ NASA + Facebook's Pytorch, Scipy, Cupy, NVIDIA, UNSW

• Facebook's Pytorch: SVD very very slow and GELS gives nans, -inf #11174 https://github.com/pytorch/pytorch/issues/11174
• Scipy: EIGH very very slow --> suggesting an easy fix #9212 https://github.com/scipy/scipy/issues/9212
• Cupy: Make SVD overwrite temporary array x https://github.com/cupy/cupy/pull/2277
• NVIDIA: Accelerating TSNE with GPUs: From hours to seconds https://medium.com/rapids-ai/tsne-with-gpus-hours-to-seconds-9d9c17c941db
• UNSW Abdussalam et al. Large-scale Sku-level Product Detection In Social Media Images And Sales Performance https://www.abstractsonline.com/pp8/#!/9305/presentation/465

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<h3> During Hyperlearn's development, bugs and issues were notified to GCC! </h3>

• GCC 10 ignoring function attribute optimize for all x86 since r11-1019 https://gcc.gnu.org/bugzilla/show_bug.cgi?id=96535
• Vector Extensions aligned(1) not generating unaligned loads/stores https://gcc.gnu.org/bugzilla/show_bug.cgi?id=98317
• GCC >= 6 cannot inline _mm_cmp_ps on SSE targets https://gcc.gnu.org/bugzilla/show_bug.cgi?id=98387
• GCC 10.2 AVX512 Mask regression from GCC 9 https://gcc.gnu.org/bugzilla/show_bug.cgi?id=98348

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<a href="" style="text-align: center"><img src="Images/Packages_Used_long.png" alt="Packages Used" align="center"/></a>

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

<a href="" style="text-align: center"><img src="Images/SVD.png" alt="Modern Big Data Algorithms" align="center"/></a>

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

<img src="Images/Preliminary Results N=5000 P=6000.png" alt="drawing" width="500"/>

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Help is really needed! Message me!

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Key Methodologies and Aims

1. Embarrassingly Parallel For Loops

2. 50%+ Faster, 50%+ Leaner

3. Why is Statsmodels sometimes unbearably slow?

4. Deep Learning Drop In Modules with PyTorch

5. 20%+ Less Code, Cleaner Clearer Code

6. Accessing Old and Exciting New Algorithms

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1. Embarrassingly Parallel For Loops

• Including Memory Sharing, Memory Management
• CUDA Parallelism through PyTorch & Numba

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2. 50%+ Faster, 50%+ Leaner

• Matrix Multiplication Ordering: https://en.wikipedia.org/wiki/Matrix_chain_multiplication
• Element Wise Matrix Multiplication reducing complexity to O(n^2) from O(n^3): https://en.wikipedia.org/wiki/Hadamard_product_(matrices)
• Reducing Matrix Operations to Einstein Notation: https://en.wikipedia.org/wiki/Einstein_notation
• Evaluating one-time Matrix Operations in succession to reduce RAM overhead.
• If p>>n, maybe decomposing X.T is better than X.
• Applying QR Decomposition then SVD might be faster in some cases.
• Utilise the structure of the matrix to compute faster inverse (eg triangular matrices, Hermitian matrices).
• Computing SVD(X) then getting pinv(X) is sometimes faster than pure pinv(X)

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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.

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4. Deep Learning Drop In Modules with PyTorch

• Using PyTorch to create Scikit-Learn like drop in replacements.

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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

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6. Accessing Old and Exciting New Algorithms

• Matrix Completion algorithms - Non Negative Least Squares, NNMF
• Batch Similarity Latent Dirichelt Allocation (BS-LDA)
• 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

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Extra License Terms

1. The Apache 2.0 license is adopted.