vectorized-computation

Efficient Batched Reinforcement Learning in TensorFlow

A tool to graphically visualize SIMD code

Another OLAP database

A curated list of awesome SIMD frameworks, libraries and software

Plot in NumPy arrays directly, overlay NumPy plots straight over video real-time, plot in Jupyter without a single loop

Fast nonlinear FEA tailored for topology optimization

An Implementation of fuzzy clustering algorithms in Numpy

Accelerating convolution using numba, cupy and xnor in python

Exercises, Descriptions, and Visualizations to build intuitions and confidence in working with PyTorch for accelerated Scientific Computing

Vectroized String Helper Functions

Two-point connectivity statistics computation for hydrological patterns

String functions using word-packing

While it is convenient to use advanced libraries for day-to-day modeling, it does not give insight into the details of what really happens underneath, when we run the codes. In this work, we implement a logistic regression model manually from scratch, without using any advanced library, to understand how it works.

A Hands-On NumPy Tutorial for Data Scientists

An ML+NLP solution for linking misspelled titles with the true titles

Collection of experiments to carve out the differences between two types of relational query processing engines: Vectorizing (interpretation based) engines and compiling engines.

Vectorized implementation of the image binarization algorithm of Su et al. (2010)

This repository shows code of programming tasks which I completed during Machine Learning course on Coursera.

Matrix multiplication speed comparison