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Applied Machine Learning

Overview of attention for book
Cover of 'Applied Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Learning to Classify
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    Chapter 2 SVMs and Random Forests
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    Chapter 3 A Little Learning Theory
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    Chapter 4 High Dimensional Data
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    Chapter 5 Principal Component Analysis
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    Chapter 6 Low Rank Approximations
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    Chapter 7 Canonical Correlation Analysis
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    Chapter 8 Clustering
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    Chapter 9 Clustering Using Probability Models
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    Chapter 10 Regression
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    Chapter 11 Regression: Choosing and Managing Models
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    Chapter 12 Boosting
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    Chapter 13 Hidden Markov Models
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    Chapter 14 Learning Sequence Models Discriminatively
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    Chapter 15 Mean Field Inference
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    Chapter 16 Simple Neural Networks
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    Chapter 17 Simple Image Classifiers
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    Chapter 18 Classifying Images and Detecting Objects
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    Chapter 19 Small Codes for Big Signals
Attention for Chapter 6: Low Rank Approximations
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