add .html files & update README

8 years ago · e49fa98a3e
--- a/README.md
+++ b/README.md
@@ -11,24 +11,29 @@ https://www.coursera.org/learn/machine-learning
 本项目包含课程中的所有课后作业以及笔记，目前正在将手写笔记进行电子化。

 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. 所有课后作业(assignments)都已提交并通过 Coursera 编程测验。仅供参考，请勿抄袭。


 笔记基于支持 LaTeX 的 markdown 编辑器 [Typora][2]，其他类似的编辑器也可以阅读，不过可能会产生一些排版问题。
 For Andrew Ng's machine learning course on Coursera.

 如要直接在 GitHub Page 上阅读，需安装 Chrome 插件 —— [GitHub with MathJax][1]，测试后发现该插件无法正常渲染某些公式（已向插件作者提出 issues），故还是建议用 Typora 阅读。
 Including assignments and notes, notes are WIP.

 You can read it by Typora or any other similar markdown editor.


 For Andrew Ng's machine learning course on Coursera.
 ## 阅读及编辑

 1. **在线阅读地址**：http://scruel.gitee.io/ml-andrewng-notes/
 2. 直接在 GitHub Page 上阅读，需安装 Chrome 插件 —— [GitHub with MathJax][1]（部分公式仍无法正常解析）。
 3. 下载 .html 网页文件及 笔记图片 image 文件夹，浏览器打开阅读。
 4. 笔记源码(.md)基于支持 LaTeX 的 markdown 编辑器 [Typora][2]，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。

 Including assignments and notes, notes are WIP.

 You can read it by Typora or any other similar markdown editor.

 ## 致谢

 - Coursera 官网文档
 - Coursera 官网
 - @小小人_V 笔记
 - 52nlp

@@ -36,7 +41,5 @@ 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！



 [1]: https://chrome.google.com/webstore/detail/ioemnmodlmafdkllaclgeombjnmnbima
 [2]: https://typora.io/
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 <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">
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 <div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">17 Large Scale Machine Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">17.1 Learning With Large Datasets</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">17.2 Stochastic Gradient Descent</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">17.3 Mini-Batch Gradient Descent</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">17.4 Stochastic Gradient Descent Convergence</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">17.5 Online Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n8"><a class="md-toc-inner" href="#header-n8">17.6 Map Reduce and Data Parallelism</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>17 Large Scale Machine Learning</h1><h2><a name='header-n3' class='md-header-anchor '></a>17.1 Learning With Large Datasets</h2><h2><a name='header-n4' class='md-header-anchor '></a>17.2 Stochastic Gradient Descent</h2><h2><a name='header-n5' class='md-header-anchor '></a>17.3 Mini-Batch Gradient Descent</h2><h2><a name='header-n6' class='md-header-anchor '></a>17.4 Stochastic Gradient Descent Convergence</h2><h2><a name='header-n7' class='md-header-anchor '></a>17.5 Online Learning</h2><h2><a name='header-n8' class='md-header-anchor '></a>17.6 Map Reduce and Data Parallelism</h2></div>
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 <div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">18 Application Example: Photo OCR</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">18.1 Problem Description and Pipeline</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">18.2 Sliding Windows</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">18.3 Getting Lots of Data and Artificial Data</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">18.4 Ceiling Analysis_ What Part of the Pipeline to Work on Next</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">Summary and Thank You</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>18 Application Example: Photo OCR</h1><h2><a name='header-n3' class='md-header-anchor '></a>18.1 Problem Description and Pipeline</h2><h2><a name='header-n4' class='md-header-anchor '></a>18.2 Sliding Windows</h2><h2><a name='header-n5' class='md-header-anchor '></a>18.3 Getting Lots of Data and Artificial Data</h2><h2><a name='header-n6' class='md-header-anchor '></a>18.4 Ceiling Analysis_ What Part of the Pipeline to Work on Next</h2><h2><a name='header-n7' class='md-header-anchor '></a>Summary and Thank You</h2></div>
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 # 18 Application Example：Photo OCR
 # 18 Application Example: Photo OCR

 ## 18.1 Problem Description and Pipeline

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 <div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">8 Neural Networks: Representation</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">8.1 Non-linear Hypotheses</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">8.2 Neurons and the Brain</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">8.3 Model Representation I</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">8.4 Model Representation II</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">8.5 Examples and Intuitions I</a></span><span class="md-toc-item md-toc-h2" data-ref="n8"><a class="md-toc-inner" href="#header-n8">8.6 Examples and Intuitions II</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">8.7 Multiclass Classification</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>8 Neural Networks: Representation</h1><h2><a name='header-n3' class='md-header-anchor '></a>8.1 Non-linear Hypotheses</h2><h2><a name='header-n4' class='md-header-anchor '></a>8.2 Neurons and the Brain</h2><h2><a name='header-n5' class='md-header-anchor '></a>8.3 Model Representation I</h2><h2><a name='header-n6' class='md-header-anchor '></a>8.4 Model Representation II</h2><h2><a name='header-n7' class='md-header-anchor '></a>8.5 Examples and Intuitions I</h2><h2><a name='header-n8' class='md-header-anchor '></a>8.6 Examples and Intuitions II</h2><h2><a name='header-n9' class='md-header-anchor '></a>8.7 Multiclass Classification</h2></div>
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 # 8 Neural Networks：Representation
 # 8 Neural Networks: Representation

 ## 8.1 Non-linear Hypotheses

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 <div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">9 Neural Networks: Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">9.1 Cost Function</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">9.2 Backpropagation Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">9.3 Backpropagation Intuition</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">9.4 Implementation Note_ Unrolling Parameters</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">9.5 Gradient Checking</a></span><span class="md-toc-item md-toc-h2" data-ref="n8"><a class="md-toc-inner" href="#header-n8">9.6 Random Initialization</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">9.7 Putting It Together</a></span><span class="md-toc-item md-toc-h2" data-ref="n10"><a class="md-toc-inner" href="#header-n10">9.8 Autonomous Driving</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>9 Neural Networks: Learning</h1><h2><a name='header-n3' class='md-header-anchor '></a>9.1 Cost Function</h2><h2><a name='header-n4' class='md-header-anchor '></a>9.2 Backpropagation Algorithm</h2><h2><a name='header-n5' class='md-header-anchor '></a>9.3 Backpropagation Intuition</h2><h2><a name='header-n6' class='md-header-anchor '></a>9.4 Implementation Note_ Unrolling Parameters</h2><h2><a name='header-n7' class='md-header-anchor '></a>9.5 Gradient Checking</h2><h2><a name='header-n8' class='md-header-anchor '></a>9.6 Random Initialization</h2><h2><a name='header-n9' class='md-header-anchor '></a>9.7 Putting It Together</h2><h2><a name='header-n10' class='md-header-anchor '></a>9.8 Autonomous Driving</h2></div>
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 # 9 Neural Networks：Learning
 # 9 Neural Networks: Learning

 ## 9.1 Cost Function

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 <div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">10 Advice for Applying Machine Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">10.1 Deciding What to Try Next</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">10.2 Evaluating a Hypothesis</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">10.3 Model Selection and Train/Validation/Test Sets</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">10.4 Diagnosing Bias vs. Variance</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">10.5 Regularization and Bias/Variance</a></span><span class="md-toc-item md-toc-h2" data-ref="n8"><a class="md-toc-inner" href="#header-n8">10.6 Learning Curves</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">10.7 Deciding What to Do Next Revisited</a></span><span class="md-toc-item md-toc-h1" data-ref="n12"><a class="md-toc-inner" href="#header-n12">11 Machine Learning System Design</a></span><span class="md-toc-item md-toc-h2" data-ref="n13"><a class="md-toc-inner" href="#header-n13">11.1 Prioritizing What to Work On</a></span><span class="md-toc-item md-toc-h2" data-ref="n14"><a class="md-toc-inner" href="#header-n14">11.2 Error Analysis</a></span><span class="md-toc-item md-toc-h2" data-ref="n15"><a class="md-toc-inner" href="#header-n15">11.3 Error Metrics for Skewed Classes</a></span><span class="md-toc-item md-toc-h2" data-ref="n16"><a class="md-toc-inner" href="#header-n16">11.4 Trading Off Precision and Recall</a></span><span class="md-toc-item md-toc-h2" data-ref="n17"><a class="md-toc-inner" href="#header-n17">11.5 Data For Machine Learning</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>10 Advice for Applying Machine Learning</h1><h2><a name='header-n3' class='md-header-anchor '></a>10.1 Deciding What to Try Next</h2><h2><a name='header-n4' class='md-header-anchor '></a>10.2 Evaluating a Hypothesis</h2><h2><a name='header-n5' class='md-header-anchor '></a>10.3 Model Selection and Train/Validation/Test Sets</h2><h2><a name='header-n6' class='md-header-anchor '></a>10.4 Diagnosing Bias vs. Variance</h2><h2><a name='header-n7' class='md-header-anchor '></a>10.5 Regularization and Bias/Variance</h2><h2><a name='header-n8' class='md-header-anchor '></a>10.6 Learning Curves</h2><h2><a name='header-n9' class='md-header-anchor '></a>10.7 Deciding What to Do Next Revisited</h2><p> </p><h1><a name='header-n12' class='md-header-anchor '></a>11 Machine Learning System Design</h1><h2><a name='header-n13' class='md-header-anchor '></a>11.1 Prioritizing What to Work On</h2><h2><a name='header-n14' class='md-header-anchor '></a>11.2 Error Analysis</h2><h2><a name='header-n15' class='md-header-anchor '></a>11.3 Error Metrics for Skewed Classes</h2><h2><a name='header-n16' class='md-header-anchor '></a>11.4 Trading Off Precision and Recall</h2><h2><a name='header-n17' class='md-header-anchor '></a>11.5 Data For Machine Learning</h2></div>
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--- a/week6.md
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@@ -1,5 +1,23 @@
 [TOC]

 # 10 Advice for Applying Machine Learning 

 ## 10.1 Deciding What to Try Next

 ## 10.2 Evaluating a Hypothesis

 ## 10.3 Model Selection and Train/Validation/Test Sets

 ##10.4 Diagnosing Bias vs. Variance

 ## 10.5 Regularization and Bias/Variance

 ## 10.6 Learning Curves

 ## 10.7 Deciding What to Do Next Revisited

 

 # 11 Machine Learning System Design

 ## 11.1 Prioritizing What to Work On
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 <div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">12 Support Vector Machines</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">12.1 Optimization Objective</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">12.2 Large Margin Intuition</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">12.3 Mathematics Behind Large Margin Classification</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">12.4 Kernels I</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">12.5 Kernels II</a></span><span class="md-toc-item md-toc-h2" data-ref="n8"><a class="md-toc-inner" href="#header-n8">12.6 Using An SVM</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>12 Support Vector Machines</h1><h2><a name='header-n3' class='md-header-anchor '></a>12.1 Optimization Objective</h2><h2><a name='header-n4' class='md-header-anchor '></a>12.2 Large Margin Intuition</h2><h2><a name='header-n5' class='md-header-anchor '></a>12.3 Mathematics Behind Large Margin Classification</h2><h2><a name='header-n6' class='md-header-anchor '></a>12.4 Kernels I</h2><h2><a name='header-n7' class='md-header-anchor '></a>12.5 Kernels II</h2><h2><a name='header-n8' class='md-header-anchor '></a>12.6 Using An SVM</h2></div>
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 <div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">13 Unsupervised Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">13.1 Unsupervised Learning_ Introduction</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">13.2 K-Means Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">13.3 Optimization Objective</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">13.4 Random Initialization</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">13.5 Choosing the Number of Clusters</a></span><span class="md-toc-item md-toc-h1" data-ref="n8"><a class="md-toc-inner" href="#header-n8">14 Dimensionality Reduction</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">14.1 Motivation I_ Data Compression</a></span><span class="md-toc-item md-toc-h2" data-ref="n10"><a class="md-toc-inner" href="#header-n10">14.2 Motivation II_ Visualization</a></span><span class="md-toc-item md-toc-h2" data-ref="n11"><a class="md-toc-inner" href="#header-n11">14.3 Principal Component Analysis Problem Formulation</a></span><span class="md-toc-item md-toc-h2" data-ref="n12"><a class="md-toc-inner" href="#header-n12">14.4 Principal Component Analysis Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n13"><a class="md-toc-inner" href="#header-n13">14.5 Reconstruction from Compressed Representation</a></span><span class="md-toc-item md-toc-h2" data-ref="n14"><a class="md-toc-inner" href="#header-n14">14.6 Choosing the Number of Principal Components</a></span><span class="md-toc-item md-toc-h2" data-ref="n15"><a class="md-toc-inner" href="#header-n15">14.7 Advice for Applying PCA</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>13 Unsupervised Learning</h1><h2><a name='header-n3' class='md-header-anchor '></a>13.1 Unsupervised Learning_ Introduction</h2><h2><a name='header-n4' class='md-header-anchor '></a>13.2 K-Means Algorithm</h2><h2><a name='header-n5' class='md-header-anchor '></a>13.3 Optimization Objective</h2><h2><a name='header-n6' class='md-header-anchor '></a>13.4 Random Initialization</h2><h2><a name='header-n7' class='md-header-anchor '></a>13.5 Choosing the Number of Clusters</h2><h1><a name='header-n8' class='md-header-anchor '></a>14 Dimensionality Reduction</h1><h2><a name='header-n9' class='md-header-anchor '></a>14.1 Motivation I_ Data Compression</h2><h2><a name='header-n10' class='md-header-anchor '></a>14.2 Motivation II_ Visualization</h2><h2><a name='header-n11' class='md-header-anchor '></a>14.3 Principal Component Analysis Problem Formulation</h2><h2><a name='header-n12' class='md-header-anchor '></a>14.4 Principal Component Analysis Algorithm</h2><h2><a name='header-n13' class='md-header-anchor '></a>14.5 Reconstruction from Compressed Representation</h2><h2><a name='header-n14' class='md-header-anchor '></a>14.6 Choosing the Number of Principal Components</h2><h2><a name='header-n15' class='md-header-anchor '></a>14.7 Advice for Applying PCA</h2></div>
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 <div  id='write'  class = 'is-node show-fences-line-number'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">15 Anomaly Detection</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">15.1 Problem Motivation</a></span><span class="md-toc-item md-toc-h2" data-ref="n4"><a class="md-toc-inner" href="#header-n4">15.2 Gaussian Distribution</a></span><span class="md-toc-item md-toc-h2" data-ref="n5"><a class="md-toc-inner" href="#header-n5">15.3 Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n6"><a class="md-toc-inner" href="#header-n6">15.4 Developing and Evaluating an Anomaly Detection System</a></span><span class="md-toc-item md-toc-h2" data-ref="n7"><a class="md-toc-inner" href="#header-n7">15.5 Anomaly Detection vs. Supervised Learning</a></span><span class="md-toc-item md-toc-h2" data-ref="n8"><a class="md-toc-inner" href="#header-n8">15.6 Choosing What Features to Use</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">15.7 Multivariate Gaussian Distribution</a></span><span class="md-toc-item md-toc-h2" data-ref="n10"><a class="md-toc-inner" href="#header-n10">15.8 Anomaly Detection using the Multivariate Gaussian Distribution</a></span><span class="md-toc-item md-toc-h1" data-ref="n11"><a class="md-toc-inner" href="#header-n11">16 Recommender Systems</a></span><span class="md-toc-item md-toc-h2" data-ref="n12"><a class="md-toc-inner" href="#header-n12">16.1 Problem Formulation</a></span><span class="md-toc-item md-toc-h2" data-ref="n13"><a class="md-toc-inner" href="#header-n13">16.2 Content Based Recommendations</a></span><span class="md-toc-item md-toc-h2" data-ref="n14"><a class="md-toc-inner" href="#header-n14">16.3 Collaborative Filtering</a></span><span class="md-toc-item md-toc-h2" data-ref="n15"><a class="md-toc-inner" href="#header-n15">16.4 Collaborative Filtering Algorithm</a></span><span class="md-toc-item md-toc-h2" data-ref="n16"><a class="md-toc-inner" href="#header-n16">16.5 Vectorization_ Low Rank Matrix Factorization</a></span><span class="md-toc-item md-toc-h2" data-ref="n17"><a class="md-toc-inner" href="#header-n17">16.6 Implementational Detail_ Mean Normalization</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>15 Anomaly Detection</h1><h2><a name='header-n3' class='md-header-anchor '></a>15.1 Problem Motivation</h2><h2><a name='header-n4' class='md-header-anchor '></a>15.2 Gaussian Distribution</h2><h2><a name='header-n5' class='md-header-anchor '></a>15.3 Algorithm</h2><h2><a name='header-n6' class='md-header-anchor '></a>15.4 Developing and Evaluating an Anomaly Detection System</h2><h2><a name='header-n7' class='md-header-anchor '></a>15.5 Anomaly Detection vs. Supervised Learning</h2><h2><a name='header-n8' class='md-header-anchor '></a>15.6 Choosing What Features to Use</h2><h2><a name='header-n9' class='md-header-anchor '></a>15.7 Multivariate Gaussian Distribution</h2><h2><a name='header-n10' class='md-header-anchor '></a>15.8 Anomaly Detection using the Multivariate Gaussian Distribution</h2><h1><a name='header-n11' class='md-header-anchor '></a>16 Recommender Systems</h1><h2><a name='header-n12' class='md-header-anchor '></a>16.1 Problem Formulation</h2><h2><a name='header-n13' class='md-header-anchor '></a>16.2 Content Based Recommendations</h2><h2><a name='header-n14' class='md-header-anchor '></a>16.3 Collaborative Filtering</h2><h2><a name='header-n15' class='md-header-anchor '></a>16.4 Collaborative Filtering Algorithm</h2><h2><a name='header-n16' class='md-header-anchor '></a>16.5 Vectorization_ Low Rank Matrix Factorization</h2><h2><a name='header-n17' class='md-header-anchor '></a>16.6 Implementational Detail_ Mean Normalization</h2></div>
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