GitHub dataset

Freyja: recover relative lineage abundances from mixed SARS-CoV-2 samples from a sequencing dataset

A GitHub action to run docker-compose

This is a short example on how to generate custom datasets for libtorch. The CustomDataset class in custom_dataset.h implements a torch::data::Dataset. It loads the image locations from file_names.csv into a std::vector, so that the CustomDataset can load images at runtime with the get method using OpenCV. You may want to change this and load all images to the RAM, since this may significantly speed up training if you are not using SSDs.

Solution to titanic competition on kaggle. Data can be either dowloaded from my GitHub profile or directly download from kaggle. The Titanic challenge on Kaggle is a competition in which the task is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat. I have been working with the Titanic dataset for a while, and I have recently achieved an accuracy score of 0.7894 on the public leaderboa

You can download our free point cloud of Wuhan University with:sh download_data.sh. Or, you can download it fromhttps://drive.google.com/open?id=10H5bhuIqdFL6iChpF0BbYjTGYqQVdXS1by yourself. Have fun!

Repository For Machine Learning Final Project. In this study, we aim to analyze and diagnose patients with Parkinson Disease (PD) by applying Machine Learning Techniques (ML) on speech datasets. In particular, we focus on applying variations of Logistic Regression, Support Vector Machines (SVM) and K-Nearest-Neighbour (KNN). The study aims to work on a previous study conducted by Istanbul University. The same datasets were used for this study and were obtained from the following UCI Database lin

RESIDE-dataset-link. We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from fu