Pinned Repositories
ecg-mit-bih
ECG classification using MIT-BIH data, a deep CNN learning implementation of Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, https://www.nature.com/articles/s41591-018-0268-3 and also deploy the trained model to a web app using Flask, introduced at
faster-rcnn.pytorch
A faster pytorch implementation of faster r-cnn
fastrcnn
demo
lintcode-AI
lintcode上AI题目的所有源代码，个人所写，非标准答案，尽力将分数刷到更高
Louplus
myFasterRCNN
FastRCNN with ResNet by torch
Neural-Headline-Generator-CN
从门户网站爬取新闻的摘要-标题对使用seq2seq根据摘要生成标题
NHG
Natural language Headline Generation自动标题生成，使用抽取式与生成式结合，用summarunner+pointer-generator多种组合，并提供网站交互式体验与对比
sumo
Eclipse SUMO is an open source, highly portable, microscopic and continuous road traffic simulation package designed to handle large road networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.
Traffic-Accident-Prediction
yongxianhuang's Repositories
yongxianhuang/ecg-mit-bih
ECG classification using MIT-BIH data, a deep CNN learning implementation of Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, https://www.nature.com/articles/s41591-018-0268-3 and also deploy the trained model to a web app using Flask, introduced at
yongxianhuang/faster-rcnn.pytorch
A faster pytorch implementation of faster r-cnn
yongxianhuang/fastrcnn
demo
yongxianhuang/lintcode-AI
lintcode上AI题目的所有源代码，个人所写，非标准答案，尽力将分数刷到更高
yongxianhuang/Louplus
yongxianhuang/myFasterRCNN
FastRCNN with ResNet by torch
yongxianhuang/Neural-Headline-Generator-CN
从门户网站爬取新闻的摘要-标题对使用seq2seq根据摘要生成标题
yongxianhuang/NHG
Natural language Headline Generation自动标题生成，使用抽取式与生成式结合，用summarunner+pointer-generator多种组合，并提供网站交互式体验与对比
yongxianhuang/sumo
Eclipse SUMO is an open source, highly portable, microscopic and continuous road traffic simulation package designed to handle large road networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.
yongxianhuang/Traffic-Accident-Prediction