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谦逊的毛巾
2025-02-10
Matrices
Learning
Linear
Vector
机器
362.4
Matrice
Inverse
ity
Norms
22.3 MB
Deep Learning
Ian Goodfellow
Yoshua Bengio
Aaron Courville
Contents
Website vii
Acknowledgments viii
Notation xi
1 Introduction 1
1.1 Who Should Read This Book? . . . . . . . . . . . . . . . . . . . . 8
1.2 Historical Trends in Deep Learning . . . . . . . . . . . . . . . . . 11
I Applied Math and Machine Learning Basics 29
2 Linear Algebra 31
2.1 Scalars, Vectors, Matrices and Tensors . . . . . . . . . . . . . . . 31
2.2 Multiplying Matrices and Vectors . . . . . . . . . . . . . . . . . . 34
2.3 Identity and Inverse Matrices . . . . . . . . . . . . . . . . . . . . 36
2.4 Linear Dependence and Span . . . . . . . . . . . . . . . . . . . . 37
2.5 Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.6 Special Kinds of Matrices and Vectors . . . . . . . . . . . . . . . 40
2.7 Eigendecomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.8 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . 44
2.9 The Moore-Penrose P
Matrices/Learning/Linear/Vector/机器/362.4/Matrice/Inverse/ity/Norms/
Matrices/Learning/Linear/Vector/机器/362.4/Matrice/Inverse/ity/Norms/
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