🎉 Deployed pages with docsify

2 years ago · 324d67d8df
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # ML-AndrewNg-Notes
 [![github](https://img.shields.io/github/stars/scruel/ML-AndrewNg-Notes?logo=github&label=Stars)](https://github.com/scruel/ML-AndrewNg-Notes)
 [![star](https://img.shields.io/badge/dynamic/json?label=Stars&query=%24.stargazers_count&url=https%3A%2F%2Fgitee.com%2Fapi%2Fv5%2Frepos%2Fscruel%2FML-AndrewNg-Notes&logo=gitee)](https://gitee.com/scruel/ML-AndrewNg-Notes)
 # Notes-ML-AndrewNg
 [![github](https://img.shields.io/github/stars/scruel/Notes-ML-AndrewNg?logo=github&label=Stars)](https://github.com/scruel/Notes-ML-AndrewNg)
 [![star](https://img.shields.io/badge/dynamic/json?label=Stars&query=%24.stargazers_count&url=https%3A%2F%2Fgitee.com%2Fapi%2Fv5%2Frepos%2Fscruel%2FNotes-ML-AndrewNg&logo=gitee)](https://gitee.com/scruel/Notes-ML-AndrewNg)

 💌 Sponsor this project: https://afdian.net/@scruel

@@ -26,7 +26,7 @@ https://www.coursera.org/learn/machine-learning

 ## Read

 1. **在线阅读地址**：http://scruel.gitee.io/ml-andrewng-notes/
 1. **在线阅读地址**：http://scruel.gitee.io/notes-ml-andrewng/

 2. 直接在 GitHub Page 上阅读，需安装 Chrome 插件 —— [GitHub with MathJax][GitHub with MathJax]（部分公式仍无法正常解析）。

@@ -47,7 +47,7 @@ https://www.coursera.org/learn/machine-learning



 如果遇到 Coursera 上提交编程作业失败的问题，以 [submitWithConfiguration.m](https://github.com/scruel/ML-AndrewNg-Notes/blob/master/assignments/submitWithConfiguration.m) 覆盖编程作业中 lib 文件夹下的同名文件即可解决。
 如果遇到 Coursera 上提交编程作业失败的问题，以 [submitWithConfiguration.m](https://github.com/scruel/Notes-ML-AndrewNg/blob/master/assignments/submitWithConfiguration.m) 覆盖编程作业中 lib 文件夹下的同名文件即可解决。

 ## Thanks

--- a/_sidebar.md
+++ b/_sidebar.md
@@ -0,0 +1,11 @@
 - [week1](/week1)
 - [week2](/week2)
 - [week3](/week3)
 - [week4](/week4)
 - [week5](/week5)
 - [week6](/week6)
 - [week7](/week7)
 - [week8](/week8)
 - [week9](/week9)
 - [week10](/week10)
 - [week11](/week11)
--- a/docsify-review.bat
+++ b/docsify-review.bat
@@ -0,0 +1,8 @@
@echo off
 REM npm i -g docsify-cli
 REM npm i -g https://github.com/scruel/docsify-tools

 REM docsify init ./docs

 call docsify-auto-sidebar -d .
 docsify serve .
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 <div  id='write'  class = 'is-node'><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 style="white-space: normal;"><a href='https://github.com/scruel/ML-AndrewNg-Notes'><img src="https://img.shields.io/github/stars/scruel/ML-AndrewNg-Notes?logo=github&amp;label=Stars" referrerpolicy="no-referrer" alt="github"></a>
 <a href='https://gitee.com/scruel/ML-AndrewNg-Notes'><img src="https://img.shields.io/badge/dynamic/json?label=Stars&amp;query=%24.stargazers_count&amp;url=https%3A%2F%2Fgitee.com%2Fapi%2Fv5%2Frepos%2Fscruel%2FML-AndrewNg-Notes&amp;logo=gitee" referrerpolicy="no-referrer" alt="star"></a></p><p><span>💌 Sponsor this project: </span><a href='https://afdian.net/@scruel' target='_blank' class='url'>https://afdian.net/@scruel</a></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-n66' 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" referrerpolicy="no-referrer" alt="Creative Commons License"></a></p><p><span>This work is licensed under a </span><a href='http://creativecommons.org/licenses/by-nc/4.0/' target='_blank'><span>Creative Commons Attribution-NonCommercial 4.0 International License</span></a><span>.</span></p><p><span>Copyright © Scruel. All Rights Reserved.</span></p><p>&nbsp;</p><p><span>本人所作的所有笔记内容</span><strong><span>禁止任何商业形式（包括广告形式）的转载</span></strong><span>，非商业形式的转载</span><strong><span>请先联系我授权</span></strong><span>，在承诺不会用于商业用途后方可使用，且需声明转载地址（GitHub）。 </span></p><p>&nbsp;</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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 <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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 <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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