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Linear Algebra and Optimization for Machine Learning

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Cover of 'Linear Algebra and Optimization for Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Linear Algebra and Optimization: An Introduction
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    Chapter 2 Linear Transformations and Linear Systems
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    Chapter 3 Eigenvectors and Diagonalizable Matrices
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    Chapter 4 Optimization Basics: A Machine Learning View
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    Chapter 5 Advanced Optimization Solutions
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    Chapter 6 Constrained Optimization and Duality
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    Chapter 7 Singular Value Decomposition
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    Chapter 8 Matrix Factorization
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    Chapter 9 The Linear Algebra of Similarity
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    Chapter 10 The Linear Algebra of Graphs
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    Chapter 11 Optimization in Computational Graphs
Attention for Chapter 3: Eigenvectors and Diagonalizable Matrices
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