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 # 1. 引言(Introduction)
 
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 # 17 Large Scale Machine Learning
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 ## 17.1 Learning With Large Datasets
 
 ## 17.2 Stochastic Gradient Descent
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 # 18 Application Example：Photo OCR
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 ## 18.1 Problem Description and Pipeline
 
 ## 18.2 Sliding Windows
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 # 4 Linear Regression with Multiple Variables
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 ## 4.1 Multiple Features
 
 ## 4.2 Gradient Descent for Multiple Variables
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 # 6 Logistic Regression
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 ## 6.1 Classification
 
 ## 6.2 Hypothesis Representation
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 # 8 Neural Networks：Representation
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 ## 8.1 Non-linear Hypotheses
 
 ## 8.2 Neurons and the Brain
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 # 9 Neural Networks：Learning
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 ## 9.1 Cost Function
 
 ## 9.2 Backpropagation Algorithm
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 # 11 Machine Learning System Design
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 ## 11.1 Prioritizing What to Work On
 
 ## 11.2 Error Analysis
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 # 12 Support Vector Machines
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 ## 12.1 Optimization Objective
 
 ## 12.2 Large Margin Intuition
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 # 13 Unsupervised Learning
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 ## 13.1 Unsupervised Learning_ Introduction
 
 ## 13.2 K-Means Algorithm
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 # 15 Anomaly Detection
+
 ## 15.1 Problem Motivation
 
 ## 15.2 Gaussian Distribution