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coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

cplusplus-_implementation_of_introduction_to_algorithms

《算法导论》第三版中算法的C++实现

cpp-primer

C++ Primer 5 answers

cpp-primer-5th-note-cn

📚 《C++ Primer中文版（第5版）》笔记

cpp-resources

A small collection of resources and notes about basic C++

cpp_primer_answers

《C++ Primer》第五版中文版习题答案

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。(Still Updating)

cpp_robotics

C++ sample codes for robotics algorithms.

cppcoreguidelines

The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++

cppprimer

:books: Solutions for C++ Primer 5th exercises.

cpprobotics

cpp implementation of robotics algorithms including localization, mapping, SLAM, path planning and control

crazyflie_human

ROS wrapper for pedestrian prediction.

cs-notes

:books: Tech Interview Guide 技术面试必备基础知识、Leetcode 题解、Java、C++、Python、后端面试、操作系统、计算机网络、系统设计

customized_navigation

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。

data-structure-and-algorithm

以前学数据结构和算法的时候写的一些程序

deep-learning

吴恩达机器学习，以及paper

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deep-trajectory-prediction

Human trajectory prediction in ROS

deepin-wine-ubuntu

Deepin Wine for Ubuntu/Debian

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

deeplearning-models

A collection of various deep learning architectures, models, and tips

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

deeplearningbook-chinese

Deep Learning Book Chinese Translation

dig-into-apollo

Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.

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。

dlut_xelatex_template_for_doctor

大连理工大学博士学位论文LaTeX模板

dlut_xelatex_template_for_maste

大连理工大学硕士学位论文LaTeX模板

dodger

Path planning in cluttered dynamic environments by predicting the motions of obstacles.

dqn_of_dwa_matlab

learning the weight of each paras in DWA(Dynamic Window Approach) by using DQN(Deep Q-Learning)