mahoo1219's repositories
Ai-Learn
人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域
CodeGuide
:books: 本代码库是作者小傅哥多年从事一线互联网 Java 开发的学习历程技术汇总，旨在为大家提供一个清晰详细的学习教程，侧重点更倾向编写Java核心内容。如果本仓库能为您提供帮助，请给予支持(关注、点赞、分享)！
EdgeCloudSim
EdgeCloudSim: An Environment for Performance Evaluation of Edge Computing Systems
EdgeSim
Simulate the real environment, perform edge computing, edge caching experiments
FCHD-Fully-Convolutional-Head-Detector
Code for FCHD - A fast and accurate head detector
Interview-Notebook-PDF
https://github.com/CyC2018/Interview-Notebook PDF版本离线阅读
java-8-lambdas-exercises
Exercises and Answers for Java 8 Lambdas book
JavaAdvance-Interview
2022java面试题、JVM面试题、多线程面试题、并发编程、Redis面试题、MySQL面试题、Java2022面试题、Netty面试题、Elasticsearch面试题、Tomcat面试题、Dubbo面试题、Kafka面试题、Linux面试题、2021面试题、java面试、2022面试题，Java面试题、设计模式面试题、Spring面试题、MyBatis面试题、Memcached面试题、MongoDB面试题、ZooKeepe面试题、RabbitMQ面试题、HTML面试题、CSS面试题、Vue面试题、React面试题、JavaScript面试题、2021java面试题、2021java面试、Java2021面试题、2022java面试
jboss-eap-quickstarts
The quickstarts demonstrate JBoss EAP, Jakarta EE 8 and a few additional technologies. They provide small, specific, working examples that can be used as a reference for your own project.
keras-yolo3
A Keras implementation of YOLOv3 (Tensorflow backend)
LANE-DETECTION-USING-DEEP-LEARNING
Autonomous self-driving is in the trend for implementing it in our real life to remove all the hassles and accidents. Modern-day transport has come a long way but still far away from perfection and all-around safety. Lane Detection is a concept of demarcating lanes on the roads while the vehicle is moving. It has the capability of changing the vehicular movements on road, making them more organized and safe. This leap could provide for driver carelessness and avoid a lot of mishaps on the roads. Ride-hailing services like Uber and Ola can use them to monitor drivers and rate them based on driving skills. We have designed and trained a deep Convolutional Network model from scratch for lane detection since a CNN based model is known to work best for image datasets. We have used BDD100k dataset for training and testing for our model. We have used various metrics values for hyper-parameters tuning and took the ones which gave the best result. The training is done on Supercomputer NVIDIA-DGX V100. Idea By: Aditya Sharma, Microsoft
leetcode
参考@CyC2018的leetcode题解。Java工程师LeetCode刷题必备。主要根据LeetCode的tag进行模块划分，每部分都选取了比较经典的题目，题目以medium和easy为主，少量hard题目。
Leetcode-1
The Solution for Leetcode
LuSTScenario
Luxembourg SUMO Traffic (LuST) Scenario
Real_time_Object_detection_and_tracking
YOLOv2 and MobileNet_SSD detection algorithms used along with KCF object tracker
reverse-interview-zh
技术面试最后反问面试官的话
SDS-Mininet-WiFi
Software Defined Systems support extension for Mininet-WiFi Emulator
sleipnir
A Monte-Carlo simulator for Mobile Edge/Cloud Computing
SSD_Tracker
Counting people, dog and bicycle using SSD detection and tracking.
WazeCCPProcessor
Waze WARP takes your CCP data feed and processes it into your cloud provider for access, analysis, and visualization. An Open Government Coalition (OGC) project. @govintheopen