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50 Page Modern Big Data Algorithms PDF

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

```diff + 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)_ ----

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

```diff + 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 ----

During Hyperlearn's development, bugs and issues were notified to GCC!

* 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 ----

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

--- ### 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 drawing

--- #### Help is really needed! Message me! --- # Key Methodologies and Aims #### 1. [Embarrassingly Parallel For Loops](#1) #### 2. [50%+ Faster, 50%+ Leaner](#2) #### 3. [Why is Statsmodels sometimes unbearably slow?](#3) #### 4. [Deep Learning Drop In Modules with PyTorch](#4) #### 5. [20%+ Less Code, Cleaner Clearer Code](#5) #### 6. [Accessing Old and Exciting New Algorithms](#6) --- ### 1. Embarrassingly Parallel For Loops * Including Memory Sharing, Memory Management * CUDA Parallelism through PyTorch & Numba ### 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) ### 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 Scikit-Learn 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 (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 --- --- # Extra License Terms 1. The Apache 2.0 license is adopted.