There are 4 repositories under vectorized-computation topic.
Efficient Batched Reinforcement Learning in TensorFlow
A tool to graphically visualize SIMD code
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
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
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.
Two-point connectivity statistics computation for hydrological patterns
Collection of experiments to carve out the differences between two types of relational query processing engines: Vectorizing (interpretation based) engines and compiling engines.
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.
Fast computation of the Boltzmann Entropy of a numerical or nominal 2-D data array.
Matrix multiplication speed comparison
A python code for demonstration of Bias-Variance TradeOff concept in Machine Learning
This is a teaching material aimed to demonstrate the powerfulness of the SPMD paradigm with MPI.
For the UNICAMP's discipline of Introduction to Digital Image Processing
In this repo I improve the performance of the LIBtft144 controller using numpy and vector operations, and use it to show in real-time the image stream from my raspberry-pi camera on a SPI 144 display
AVS - Computation Systems Architectures - Project: Sequence code optimization (vectorization)
Computation Systems Architectures - Project - Sequence code optimization
Поиск диссонансов временного ряда
Architektury výpočetních systémů - Cvičení
Architektury výpočetních systémů - Projekt - Optimalizace sekvenčního kódu
A way to speed up gradient descent is having each feature in the same range.
This repository includes my solutions to the Machine Learning Assignments offered on coursera.
Thomas Wang's random number generation function implicitly parallelized & pipelined at speed of 0.6 cycles per 32bit integer.