scruel
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| @@ -55,14 +55,17 @@ You can read it by Typora or any other similar markdown editor. | |||
| 注：由于手写笔记成稿时间较早，有所参考无法一一致谢，在此统一表示谢意！ | |||
| ## License | |||
| [][3] | |||
| [本(ke)人(chi)的知乎笔记文案](https://zhuanlan.zhihu.com/p/32781741) | |||
| This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][3]. | |||
| [][3] | |||
| [知乎文章](https://zhuanlan.zhihu.com/p/32781741) | |||
| This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][3]. | |||
| By: Scruel | |||
| [1]: https://chrome.google.com/webstore/detail/ioemnmodlmafdkllaclgeombjnmnbima | |||
| [2]: https://typora.io/ | |||
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| <html> | |||
| <head> | |||
| <meta charset='UTF-8'><meta name='viewport' content='width=device-width initial-scale=1'> | |||
| <title>吴恩达(Andrew Ng)机器学习公开课中文笔记</title><link href='http://fonts.googleapis.com/css?family=Open+Sans:400italic,700italic,700,400&subset=latin,latin-ext' rel='stylesheet' type='text/css' /><style type='text/css'>html {overflow-x: initial !important;}:root { --bg-color: #ffffff; --text-color: #333333; --code-block-bg-color: inherit; } | |||
| <title>index.md</title><link href='http://fonts.googleapis.com/css?family=Open+Sans:400italic,700italic,700,400&subset=latin,latin-ext' rel='stylesheet' type='text/css' /><style type='text/css'>html {overflow-x: initial !important;}:root { --bg-color: #ffffff; --text-color: #333333; --code-block-bg-color: inherit; } | |||
| html { font-size: 14px; background-color: var(--bg-color); color: var(--text-color); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -webkit-font-smoothing: antialiased; } | |||
| body { margin: 0px; padding: 0px; height: auto; bottom: 0px; top: 0px; left: 0px; right: 0px; font-size: 1rem; line-height: 1.42857; overflow-x: hidden; background: inherit; } | |||
| a:active, a:hover { outline: 0px; } | |||
| @@ -220,7 +220,7 @@ header, .context-menu, .megamenu-content, footer { font-family: "Segoe UI", Aria | |||
| </style> | |||
| </head> | |||
| <body class='typora-export' > | |||
| <div id='write' class = 'is-node show-fences-line-number'><h1><a name='header-n282' class='md-header-anchor '></a>吴恩达(Andrew Ng)机器学习公开课中文笔记</h1><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><div style="display:none"> | |||
| <div id='write' class = 'is-node show-fences-line-number'><h1><a name='header-n284' class='md-header-anchor '></a>吴恩达(Andrew Ng)机器学习公开课中文笔记</h1><p><a href='https://github.com/scruel/ML-AndrewNg-Notes/'>GitHub 项目首页</a></p><p> </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> </p><p> </p><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> </p><p><a href='https://zhuanlan.zhihu.com/p/32781741'>知乎文章</a></p><p>By: Scruel</p><p> </p><p><div style="display:none"> | |||
| <script src="https://s19.cnzz.com/z_stat.php?id=1272117433&web_id=1272117433" language="JavaScript"></script> | |||
| </div></p></div> | |||
| </body> | |||
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| @@ -61,6 +61,8 @@ BrainPort 系统：帮助失明人士通过摄像头以及舌尖感官“看” | |||
| >$\Theta^{(j)}$: 从第 $j$ 层映射到第 $j+1$ 层时的权重矩阵。 | |||
| > | |||
| >$\Theta^{(j)}_{v,u}$: 从第 $j$ 层的第 $u$ 个单元映射到第 $j+1$ 层的第 $v$ 个单元的权重 | |||
| > | |||
| >$s_j$: 第 $j$ 层的激活单元数目（不包含偏置单元） | |||
| 注意： | |||