cuVS - a library for vector search and clustering on the GPU

RAFT contains fundamental widely-used algorithms and primitives for data science, graph and machine learning.

This is a set of simple programs that can be used to explore the features of a parallel platform.

Source code examples from the Parallel Forall Blog

cuDF - GPU DataFrame Library

cuML - RAPIDS Machine Learning Library

A benchmark to measure performance of popular Gradient boosting algorithms against popular ML datasets.

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

C GPU Dataframe Library

Image-to-image translation in PyTorch (e.g., horse2zebra, edges2cats, and more)

TensorFlow implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".

Code from various chapters in OSTEP (http://www.ostep.org)

A tensorflow implement of the paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"

Tensorflow implementation of the SRGAN algorithm for single image super-resolution

Tensorflow implementation for learning an image-to-image translation without input-output pairs. https://arxiv.org/pdf/1703.10593.pdf

Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!

Example Makefile for CUDA and C++ source files in a standard project layout.