qingshanwang Goto Github PK
Type: User
Location: HZ
qingshanwang's Projects
-million-song-dataset-challenge
For MSD challenge build model use a smaller data set.
100-days-of-ml-code
100-Days-Of-ML-Code中文版
2019-autumn-recruitment-experience
2019届秋招面经集合
2023_cpp_questions
2023年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。
ai-roadmap
ApacheCN AI 路线图（知识树）
ailearning
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
algorithm_interview_notes-chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
awesome-deep-learning-and-machine-learning-questions
【不定期更新中】收集整理的一些网站中（如知乎、Quora、Reddit、Stack Exchange等）与深度学习、机器学习、强化学习、数据科学相关的有价值的问题
c-_question
2021年最新整理，5000道秋招/提前批/春招/常用面试题（含答案），包括leetcode，校招笔试题，面试题，算法题，语法题。
client-server-webrtc-example
An example for using WebRTC to communicate between a JavaScript client and a C++ server.
cpp-learning
学习C++的资料
dash-docset-tensorflow
dash/zeal docset for tensorflow
data-science-study-paths-march-2019
A complete ML study path, focused on TensorFlow and Scikit-Learn
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.ai
deeplearning.ai , By Andrew Ng, All slide and notebook + code and some material.
desktoplivestreaming
DesktopLiveStreaming
interview_python
关于Python的面试题
linux-ipcs
Linux进程间通信(Inter-Process Communication)方式汇总
machine-learning-interview-qa
人工智能-机器学习笔试面试题解析
opencv
Open Source Computer Vision Library
peerconnectionclientdemo
PeerConnectionClient vs2017 Demo
pilot
Pilot – mini game engine for games104
python-100-days
Python - 100天从新手到大师
python_interview_question
关于python的面试题
tensorflow-course
Simple and ready-to-use tutorials for TensorFlow
webrtc-build
WebRTC auto build scripts for windows/mac/linux/iOS/Android
webrtc-precompiled-builds
WebRTC precompiled builds for Linux and Windows.
webrtctest
webRTCtest-linux
widendeep
wide and deep labs and samples
xlearn
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
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