C0ra1's Stars
Snailclimb/JavaGuide
「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！
labuladong/fucking-algorithm
刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.
iluwatar/java-design-patterns
Design patterns implemented in Java
scutan90/DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
fengdu78/deeplearning_ai_books
deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）
thunlp/GNNPapers
Must-read papers on graph neural networks (GNN)
Mikoto10032/DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
dair-ai/ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
SSHeRun/CS-Xmind-Note
计算机专业课（408）思维导图和笔记：计算机组成原理（第五版 王爱英），数据结构（王道），计算机网络（第七版 谢希仁），操作系统（第四版 汤小丹）
ZhongFuCheng3y/austin
消息推送平台🔥 推送下发【邮件】【短信】【微信服务号】【微信小程序】【企业微信】【钉钉】等消息类型。
lilishop/lilishop
商城 JAVA电商商城 多语言商城 uniapp商城 微服务商城
thunlp/PromptPapers
Must-read papers on prompt-based tuning for pre-trained language models.
wususu/effective-resourses
:book:学习资源整合
Xovee/uestc-course
电子科技大学课程资料共享平台. Course material sharing platform of UESTC.
MikeCreken/lanlanInterview
此仓库将包含各大银行的基本介绍，笔试面试特点，发现这个宝库就离上岸不远了，哼
erdengk/excellent-wheel
收集轮子类项目
LibCity/Bigscity-LibCity
LibCity: An Open Library for Urban Spatial-temporal Data Mining
he2121/MyRPCFromZero
从零开始，手写一个RPC，任何人都能看懂
zezhishao/BasicTS
A Standard and Fair Time Series Forecasting Benchmark and Toolkit.
LeiBAI/AGCRN
Adaptive Graph Convolutional Recurrent Network
PaddlePaddle/PaddleSpatial
PaddleSpatial is an open-source spatial-temporal computing tool based on PaddlePaddle.
zezhishao/STEP
Code for our SIGKDD'22 paper Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting.
xiaoxiong74/TrafficFlowForecasting
Some TrafficFlowForecasting Solutions(交通流量预测解决方案)
aprbw/traffic_prediction
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).
QiuYukang/JavaNotesForInterview
Java面试经验、面试技巧、常见面试知识点整理。
LeronQ/GCN_predict-Pytorch
Traffic flow predict. Implementation of graph convolutional network with PyTorch
fycm123/postgraduate-curriculum-uestc
:heart:电子科技大学信息与软件工程研究生课程资料（Postgraduate PPT and Homework of UESTC-2019）
sunilkumarmaurya786693/Intelligence-traffic-monitoring-system
# Intelligence traffic monitoring system ### About Due to a huge number of vehicles ,very busy road and parking which may not be possible manually as a human being, tends to get fatigued due to monotonous nature of the job and they cannot keep track of the vehicles when there are multiple vehicles are passing in a very short time. So modern cities need to establish effective automatic systems for traffic management and scheduling. The objective of this project is to design and develop an accurate and automatic number plate recognition system, Automatic traffic light control using google Api live traffic density data, smart fine system and also We can track the lost vehicle using vehicle number plate detection and find its location by google Map API. Intelligent Traffic Monitoring System (ITMS) is an image processing and machine learning technology to identify vehicles by their license plates and we uses the microService of google API for live traffic density. ### Features 1. License plate number recognition. 2. Matching the plate number with Database. 3. Intelligence traffic light control using live traffic density data. 4. Show traffic density of particular area for some duration of month in form of graph. 5. Online Vehicle license registration. 6. Smart fine system. ###Applications 1. Automated track the location of stolen vehicle 2. Anti-Theft/ Vehicle detection. 3. Traffic light automation ,no requirement of Traffic police. 4. Smart fine /E Challan Systems. 5. Car Parking / Automatic Toll Deduction. 6. Law Enforcement 7. VIP/Ambulance path Clearance 8. Help the government to take ● Increase the efficiency of existing transport infrastructure ● Develop a license plate recognition system, ● Build a smart fine system and in future enhancement automated fine systems for vehicles. ● Live Traffic detection system and automated traffic light control system. ● Predict the traffic density using machine learning for specific areas by its previous data. ● Automated lost vehicle detection system and information to administration. ● Handle traffic congestion using automated light control system. ### Installation * Clone the project. * Run `yarn install` to install the dependencies. * Run `yarn start` to view the project in action. ### OpenCV Demo to Count Vehicles * In "countingCars" directory, run 'python count.py' . ### License plate detection go to vehicle_number_by_its_pate folder and type python3 licenseplateDetection.py 1.jpg #secreenshot <img src="./screenshot/IMG_20200901_103735.jpg"> <img src="./screenshot/IMG_20200901_103751.jpg"> <img src="./screenshot/IMG_20200901_103811.jpg"> <img src="./screenshot/IMG_20200901_103826.jpg"> <img src="./screenshot/IMG_20200901_103844.jpg"> <img src="./screenshot/IMG_20200901_103906.jpg"> <img src="./screenshot/IMG_20200901_103943.jpg"> <img src="./screenshot/IMG_20200901_104003.jpg"> <img src="./screenshot/IMG_20200901_104044.jpg"> <img src="./screenshot/IMG_20200902_032314.jpg">
taiwotman/Smart-Traffic
A system and method for the prediction of vehicle traffic congestion on a given roadway within a region. In particular, the computer implemented method of the present disclosure utilize real time traffic images from traffic cameras for the input of data and utilizes computer processing and machine learning to model a predictive level of congestion within a category of low congestion, medium congestion, or high congestion. By implementing machine learning in the comparison of exemplary images and administrator review, the computer processing system and method steps can predict a more efficient real-time congestion prediction over time.
daniMusli/roadTrafficForecast
Road Traffic forecasting system on video data built with keras and deployed with flask :