# Markdown 이란?
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Markdown은 텍스트 기반의 마크업언어
마크다운이 최근 각광받기 시작한 이유는 깃헙(https://github.com) 덕분이다.
README.md는 깃헙을 사용하는 사람이라면 누구나 가장 먼저 접하게 되는 마크다운 문서였다.
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t9RSx6
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README.md
- snippet.markdown
<div align="center"> <img src="https://www.tensorflow.org/images/tf_logo_transp.png"><br><br> </div> ----------------- | **`Linux CPU`** | **`Linux GPU`** | **`Mac OS CPU`** | **`Windows CPU`** | **`Android`** | |-----------------|---------------------|------------------|-------------------|---------------| | [](https://ci.tensorflow.org/job/tensorflow-master-cpu) | [](https://ci.tensorflow.org/job/tensorflow-master-linux-gpu) | [](https://ci.tensorflow.org/job/tensorflow-master-mac) | [](https://ci.tensorflow.org/job/tensorflow-master-win-cmake-py) | [](https://ci.tensorflow.org/job/tensorflow-master-android) | **TensorFlow** is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well. **If you want to contribute to TensorFlow, be sure to review the [contribution guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's [code of conduct](CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code.** **We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for tracking requests and bugs. So please see [TensorFlow Discuss](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) for general questions and discussion, and please direct specific questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).** .. 이하 생략 ..
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## 마크다운의 장점
<fragment-block fade-up>1. 간결하다.</fragment-block>
<fragment-block fade-up>2. 별도의 도구없이 작성가능하다.</fragment-block>
<fragment-block fade-up>3. 다양한 형태로 변환이 가능하다.</fragment-block>
<fragment-block fade-up>4. 텍스트(Text)로 저장되기 때문에 용량이 적어 보관이 용이하다.</fragment-block>
<fragment-block fade-up>5. 텍스트파일이기 때문에 버전관리시스템을 이용하여 변경이력을 관리할 수 있다.</fragment-block>
<fragment-block fade-up>6. 지원하는 프로그램과 플랫폼이 다양하다.</fragment-block>
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마크다운의 단점
<fragment-block fade-up>1. 표준이 없다.</fragment-block>
<fragment-block fade-up>2. 표준이 없기 때문에 도구에 따라서 변환방식이나 생성물이 다르다.</fragment-block>
<fragment-block fade-up>3. 모든 HTML 마크업을 대신하지 못한다.</fragment-block>
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## 마크다운 사용법
- Headers 해더
- BlockQuote 인용구
- list 목록
- code 코드
- link 링크
- strong 강조
- image 이미지
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Headers 해더
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입력
- snippet.html
# This is a H1 ## This is a H2 ### This is a H3 #### This is a H4 ##### This is a H5 ###### This is a H6
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결과
This is a H1
This is a H2
This is a H3
This is a H4
This is a H5
This is a H6
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BlockQuote 인용구
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입력
- snippet.html
> This is a blockqute.
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결과
This is a blockqute.
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list 목록
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입력
- snippet.html
- 첫번째 - 두번째 - 세번째
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결과
- 첫번째
- 두번째
- 세번째
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code 코드
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입력
- snippet.html
``` This is a code block. ```
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결과
- snippet.html
This is a code block.
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link 링크
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입력
- snippet.html
syntax: [Title](link) [google](https://www.google.co.kr)
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결과
syntax: Title
google
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strong 강조
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입력
- snippet.md
*single asterisks* _single underscores_ **double asterisks** __double underscores__ ++underline++ ~~cancelline~~
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결과
single asterisks
single underscores
double asterisks
double underscores
++underline++
~~cancelline~~
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image 이미지
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입력
- snippet.html
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결과
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마크다운을 사용할 수 있는 Site
Github
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Gitbook
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마크다운을 사용할 수 있는 Application
Typora
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BoostNote
# 마크다운 온라인 실습
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