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
Fast nonlinear FEA tailored for topology optimization
Plot in NumPy arrays directly, overlay NumPy plots straight over video real-time, plot in Jupyter without a single loop
Vectroized String Helper Functions
An Implementation of fuzzy clustering algorithms in Numpy
Exercises, Descriptions, and Visualizations to build intuitions and confidence in working with PyTorch for accelerated Scientific Computing
Accelerating convolution using numba, cupy and xnor in python
Two-point connectivity statistics computation for hydrological patterns
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.
An ML+NLP solution for linking misspelled titles with the true titles
Vectorized implementation of the image binarization algorithm of Su et al. (2010)
A Hands-On NumPy Tutorial for Data Scientists
This repository shows code of programming tasks which I completed during Machine Learning course on Coursera.
Collection of experiments to carve out the differences between two types of relational query processing engines: Vectorizing (interpretation based) engines and compiling engines.
Matrix multiplication speed comparison
Add a description, image, and links to the vectorized-computation topic page so that developers can more easily learn about it.
To associate your repository with the vectorized-computation topic, visit your repo's landing page and select "manage topics."