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update README

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scruel 7 years ago
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README.md View File

@@ -13,22 +13,9 @@ https://www.coursera.org/learn/machine-learning
1. 笔记(notes)都为中文，为了便于复习和扩充等，尽量会按照视频目录，以及视频内容进行提炼整理。
2. 遵循[荣誉准则][honor code]，现已移除已通过的课后编程作业的源代码，之后会改为指导笔记形式陆续更新。

感谢关注，欢迎分享。



电子版笔记基于手写笔记，时间有限再加上为了追求清晰精确，更新较慢请大佬们谅解😭。

码的很辛苦，大佬们如果觉得笔记整理的还不错，记得保持关注，也欢迎分享哦。

感谢支持(\*^_^\*)。



For Andrew Ng's machine learning course on Coursera.

Including assignments and notes, notes are WIP.

You can read it by Typora or any other similar markdown editor.
[知乎文章][zhihu]


## Read
@@ -42,8 +29,6 @@ You can read it by Typora or any other similar markdown editor.
4. 笔记源码(.md)基于支持 LaTeX 的 markdown 编辑器 [Typora][Typora]，其他类似的编辑器也可以阅读及编辑，注意其他编辑器可能会产生排版问题。




## Resources

方便网络不方便的同学学习
@@ -72,13 +57,17 @@ You can read it by Typora or any other similar markdown editor.

This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][CC BY-NC 4.0].

Copyright © Scruel. All Rights Reserved.

**注：转载请注明出处，禁止用于任何商业用途（包括广告形式）**

Copyright © Scruel. All Rights Reserved.
## Donate

觉得不错，不如请我喝杯咖啡：
![image](image/20181115103549.png)



[知乎文章][zhihu]

[zhihu]: https://zhuanlan.zhihu.com/p/32781741
[baidupan]: https://pan.baidu.com/s/1mkmnRIC


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<div id='write' class = 'is-node show-fences-line-number'><h1><a name='header-n0' class='md-header-anchor '></a>吴恩达(Andrew Ng)机器学习公开课中文笔记</h1><p>电子版笔记基于手写笔记，时间有限再加上为了追求清晰精确，更新较慢请大佬们谅解😭。</p><p>码的很辛苦，大佬们如果觉得笔记整理的还不错，记得保持关注，也欢迎分享哦。</p><p>感谢支持(*^_^*)。</p><p>有打赏需求的大佬们，<del>支付宝账号即为 GitHub 主页邮箱--</del>，嗯……应该没有吧。</p><p><a href='https://github.com/scruel/ML-AndrewNg-Notes/'>GitHub 项目首页</a> | <a href='https://zhuanlan.zhihu.com/p/32781741'>知乎文章</a></p><p>&nbsp;</p><p><a href='./week1.html'>week1</a></p><ol start='' ><li>引言(Introduction)</li><li>单变量线性回归(Linear Regression with One Variable)</li></ol><p><a href='./week2.html'>week2</a></p><ol start='3' ><li>线性代数回顾(Linear Algebra Review)</li><li>多变量线性回归(Linear Regression with Multiple Variables)</li><li>Octave/Matlab 指南(Octave/Matlab Tutorial)</li></ol><p><a href='./week3.html'>week3</a></p><ol start='6' ><li>逻辑回归(Logistic Regression)</li><li>正则化(Regularization)</li></ol><p><a href='./week4.html'>week4</a></p><ol start='8' ><li>神经网络：表达(Neural Networks: Representation)</li></ol><p><a href='./week5.html'>week5</a></p><ol start='9' ><li>神经网络：学习(Neural Networks: Learning)</li></ol><p><a href='./week6.html'>week6</a></p><ol start='10' ><li>机器学习应用的建议(Advice for Applying Machine Learning)</li><li>机器学习系统设计(Machine Learning System Design)</li></ol><p><a href='./week7.html'>week7</a></p><ol start='12' ><li>支持向量机(Support Vector Machines)</li></ol><p><a href='./week8.html'>week8</a></p><ol start='13' ><li>无监督学习(Unsupervised Learning)</li><li>降维(Dimensionality Reduction)</li></ol><p><a href='./week9.html'>week9</a></p><ol start='15' ><li>异常检测(Anomaly Detection)</li><li>推荐系统(Recommender Systems)</li></ol><p><a href='./week10.html'>week10</a></p><ol start='17' ><li>大规模机器学习(Large Scale Machine Learning)</li></ol><p><a href='./week11.html'>week11</a></p><ol start='18' ><li>实战：图像光学识别(Application Example: Photo OCR)</li></ol><p>&nbsp;</p><p>&nbsp;</p><h2><a name='header-n103' class='md-header-anchor '></a>License</h2><p><a href='http://creativecommons.org/licenses/by-nc/4.0/' target='_blank'><img src='https://i.creativecommons.org/l/by-nc/4.0/88x31.png' alt='Creative Commons License' /></a></p><p>This work is licensed under a <a href='http://creativecommons.org/licenses/by-nc/4.0/' target='_blank'>Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p><p>&nbsp;</p><p>By: Scruel</p><p>&nbsp;</p><p><div style="display:none">
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<div id='write' class = 'is-node show-fences-line-number'><h1><a name='header-n123' class='md-header-anchor '></a>吴恩达(Andrew Ng)机器学习公开课中文笔记</h1><p>感谢关注，欢迎分享。</p><p><a href='https://github.com/scruel/ML-AndrewNg-Notes/'>GitHub 项目首页</a> | <a href='https://zhuanlan.zhihu.com/p/32781741'>知乎文章</a></p><p>&nbsp;</p><p><a href='./week1.html'>week1</a></p><ol start='' ><li>引言(Introduction)</li><li>单变量线性回归(Linear Regression with One Variable)</li></ol><p><a href='./week2.html'>week2</a></p><ol start='3' ><li>线性代数回顾(Linear Algebra Review)</li><li>多变量线性回归(Linear Regression with Multiple Variables)</li><li>Octave/Matlab 指南(Octave/Matlab Tutorial)</li></ol><p><a href='./week3.html'>week3</a></p><ol start='6' ><li>逻辑回归(Logistic Regression)</li><li>正则化(Regularization)</li></ol><p><a href='./week4.html'>week4</a></p><ol start='8' ><li>神经网络：表达(Neural Networks: Representation)</li></ol><p><a href='./week5.html'>week5</a></p><ol start='9' ><li>神经网络：学习(Neural Networks: Learning)</li></ol><p><a href='./week6.html'>week6</a></p><ol start='10' ><li>机器学习应用的建议(Advice for Applying Machine Learning)</li><li>机器学习系统设计(Machine Learning System Design)</li></ol><p><a href='./week7.html'>week7</a></p><ol start='12' ><li>支持向量机(Support Vector Machines)</li></ol><p><a href='./week8.html'>week8</a></p><ol start='13' ><li>无监督学习(Unsupervised Learning)</li><li>降维(Dimensionality Reduction)</li></ol><p><a href='./week9.html'>week9</a></p><ol start='15' ><li>异常检测(Anomaly Detection)</li><li>推荐系统(Recommender Systems)</li></ol><p><a href='./week10.html'>week10</a></p><ol start='17' ><li>大规模机器学习(Large Scale Machine Learning)</li></ol><p><a href='./week11.html'>week11</a></p><ol start='18' ><li>实战：图像光学识别(Application Example: Photo OCR)</li></ol><p>&nbsp;</p><p>&nbsp;</p><h2><a name='header-n229' class='md-header-anchor '></a>License</h2><p>[<img src='https://i.creativecommons.org/l/by-nc/4.0/88x31.png' alt='Creative Commons License' referrerPolicy='no-referrer' />][CC BY-NC 4.0]</p><p>This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][CC BY-NC 4.0].</p><p>Copyright © Scruel. All Rights Reserved.</p><p><strong>注：转载请注明出处，禁止用于任何商业用途（包括广告形式）</strong></p><h2><a name='header-n238' class='md-header-anchor '></a>Donate</h2><p>觉得不错，不如请我喝杯咖啡：
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