Deep Learning Book Series 3.4 and 3.5 Marginal and Conditional Probability
⥈ ⥈ ⥈Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions
⥈ ⥈ ⥈Preprocessing for deep learning: from covariance matrix to image whitening
⥈ ⥈ ⥈Deep Learning Book Series · 2.12 Example Principal Components Analysis
⥈ ⥈ ⥈Deep Learning Book Series · 2.11 The determinant
⥈ ⥈ ⥈Deep Learning Book Series · 2.10 The Trace Operator
⥈ ⥈ ⥈Deep Learning Book Series · 2.9 The Moore Penrose Pseudoinverse
⥈ ⥈ ⥈Deep Learning Book Series · 2.8 Singular Value Decomposition
⥈ ⥈ ⥈Deep Learning Book Series · 2.7 Eigendecomposition
⥈ ⥈ ⥈Deep Learning Book Series · 2.6 Special Kinds of Matrices and Vectors
⥈ ⥈ ⥈Deep Learning Book Series · 2.5 Norms
⥈ ⥈ ⥈Deep Learning Book Series · 2.4 Linear Dependence and Span
⥈ ⥈ ⥈Deep Learning Book Series · 2.3 Identity and Inverse Matrices
⥈ ⥈ ⥈Deep Learning Book Series · 2.2 Multiplying Matrices and Vectors
⥈ ⥈ ⥈Deep Learning Book Series · 2.1 Scalars Vectors Matrices and Tensors
⥈ ⥈ ⥈Deep Learning Book Series · Introduction
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