1.建立项目,进入项目文件夹
2.初始化ssh key 参见官网指南.(本机生成一对key,public key传到官网sshkey下面)
https://help.github.com/articles/generating-ssh-keys/
3.初始化用户名,邮箱
$ git config --global user.name "defnngj"//给自己起个用户名
$ git config --global user.email "defnngj@gmail.com"//填写自己的邮箱
4.git init
5.git add .
6.git commit -m "message for this commit"
7.touch README.md
8.git add README.md
9.git status
10.去github网站建立一个repo 如"ts.git"
11.git remote add origin git@github.com:wuzhuzhu/ts.git
如果遇到了fatal: remote origin already exists.输入:
git remote rm origin
12.git remote add origin git@github.com:wuzhuzhu/ts.git
13.git push -u origin master
Counting objects: 19, done.
Compressing objects: 100% (16/16), done.
Writing objects: 100% (19/19), 4.54 KiB, done.
Total 19 (delta 1), reused 0 (delta 0)
To git@github.com:wuzhuzhu/ts.git
[new branch] master -> master
Branch master set up to track remote branch master from origin.
14.拉取git文档:
在远程主机上:
git remote add origin git@github.com:wuzhuzhu/ts.git
git pull origin master
遇见的问题:
windows 客户端是渣...还是要用git shell 要不连创建repo都总是网络报错 远程服务器要搞定ssh key... git
remote add origin git@github.com:wuzhuzhu/ts.git
是指制定origin到这个git网址,不要重复绑定.
以上就是个人简化版的github配置了,抛砖引玉,给小伙伴们参考下

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.


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