imhewon Goto Github PK

2020

Hanyang University MNI Lab (AI, ML & Network), Hong-Sik Kim

ai_uav

在人工智能、机器视觉、高精度导航定位和多传感器融合等技术的助推下，众多行业迎来了前所未有的发展机遇，人工智能+无人机（AI+UAV）正是一个具有无限想象力的应用方向。

arxivtimes

repository to research & share the machine learning articles

assistant-optimization

awsome-front-end-learning-resource

:octocat:GitHub最全的前端资源汇总仓库（包括前端学习、开发资源、求职面试等）

cloud2021

code-hewon

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

cov

cov-native

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++

daily-interview-question

我是木易杨，公众号「高级前端进阶」作者，每天搞定一道前端大厂面试题，祝大家天天进步，一年后会看到不一样的自己。

dailyrecord

data-cleaning

数据清洗系统；hadoop；实体识别；冲突消解；不一致修复；缺失值填充

deep_rl_with_pytorch

A pytorch tutorial for DRL(Deep Reinforcement Learning)

deeprl

Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

dqn-tensorflow

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

dqn_zoo

DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.

drl_uav

Deep reinforcement learning for UAV in Gazebo simulation environment

drlpytorch

Pytorch for Deep Reinforcement Learning

faster-kmeans

Code for a faster K-means clustering heuristic

frenet_path_planning

无人车路径规划算法demo

front-end-roadmap

Tell you how to learn front end development ~

gym-uav

A uav navigation simulator in large-scale complex environments

hellogit

hewang23.github.io

blog & blog theme🤘

hhe.github.io

imhewon.github.io

本项目为Pages静态博客示范，对应的演示网站地址为 👉

intelligentuavpathplanningsimulationsystem-drone

Intelligent UAV path planning simulation system is a software with fine operation control, strong platform integration, omnidirectional model building and application automation. It takes the UAV war between A and B in Zone C as the background. The core function of the system is to plan the UAV route through the simulation platform and verify the output. The data can be imported into the real UAV to make it accurately arrive at any position in the battlefield according to the specified route and support the joint action of multi-person and multi-device formation.