dunerdou Goto Github PK

6-pack

"6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints" code repository

awesome-trajectory-motion-prediction-papers

canopensocket

Obsolete repository, replaced by CANopenDemo and CANopenLinux

cpp_primer_practice

搞定C++:punch:。C++ Primer 中文版第5版学习仓库，包括笔记和课后练习答案。

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deep-learning-papers-translation

深度学习论文翻译，包括分类论文，检测论文等

deep_object_pose

Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018)

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[email protected] 版权所有，违权必究 Tan 2018.06

densefusion

"DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion" code repository

dunerdou.github.io

本项目为Pages静态博客示范，对应的演示网站地址为 👉

effectivemoderncppchinese

《Effective Modern C++》翻译 - 已完成

fluent-python

《流畅的Python》2015年8月

fucking-algorithm-book

《labuladong的算法小抄》顺序阅读版

gbml

A collection of Gradient-Based Meta-Learning Algorithms with pytorch

gemma_pytorch

The official PyTorch implementation of Google's Gemma models

grips

Gradient-Informed Path Smoothing for Wheeled Mobile Robots (GRIPS)

install

一键安装程序，欢迎大家提交代码和小鱼一起一键安装停止浪费生命

java

存放JAVA开发的设计**、算法：《剑指Offer》、《编程珠玑》、《深入理解Java虚拟机：JVM高级特性与最佳实践》、《重构-改善既有代码的设计 中文版》、《clean_code(中文完整版)》、《Java编程**(第4版)》、《Java核心技术 卷I (第8版)》、《Quartz_Job+Scheduling_Framework》；一些大的上传不上来的文件在README

java-review-gudie

【Java快速面试指南】Java基础、异常、集合、并发编程、JVM、Spring全家桶、MyBatis、Redis、数据库、中间件MQ、Dubbo、Linux、Tomcat、ZooKeeper、Netty等等。

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！

lihang-code

《统计学习方法》的代码实现

machine-learning-book-pirated

机器学习中深度学习、强化学习、监督学习、集成学习相关的影印版pdf书籍

maml-pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

motion-plan

深蓝运动规划课程

mvcnn_fewshot

my_navigation

nonframework

notes

一些笔记

object-rpe

objectron

Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes