Linear-algebra
- Deep Learning Book Series · 2.12 Example Principal Components Analysis 26-03-2018
- Deep Learning Book Series · 2.11 The determinant 26-03-2018
- Deep Learning Book Series · 2.10 The Trace Operator 26-03-2018
- Deep Learning Book Series · 2.9 The Moore Penrose Pseudoinverse 26-03-2018
- Deep Learning Book Series · 2.8 Singular Value Decomposition 26-03-2018
- Deep Learning Book Series · 2.7 Eigendecomposition 26-03-2018
- Deep Learning Book Series · 2.6 Special Kinds of Matrices and Vectors 26-03-2018
- Deep Learning Book Series · 2.5 Norms 26-03-2018
- Deep Learning Book Series · 2.4 Linear Dependence and Span 26-03-2018
- Deep Learning Book Series · 2.3 Identity and Inverse Matrices 26-03-2018
- Deep Learning Book Series · 2.2 Multiplying Matrices and Vectors 26-03-2018
- Deep Learning Book Series · 2.1 Scalars Vectors Matrices and Tensors 26-03-2018
- Deep Learning Book Series · Introduction 26-03-2018
Deep-learning
- Preprocessing for deep learning: from covariance matrix to image whitening 27-08-2018
- Deep Learning Book Series · Introduction 26-03-2018
Machine-learning
- Deep Learning Book Series · Introduction 26-03-2018
Python
- Essential Math for Data Science: Visual Introduction to Singular Value Decomposition (SVD) 17-04-2021
- Essential Math for Data Science: Eigenvectors and application to PCA 23-02-2021
- Essential Math for Data Science: Basis and Change of Basis 01-02-2021
- Essential Math for Data Science: Introduction to Systems of Linear Equations 13-01-2021
- Essential Math for Data Science: Linear Transformation with Matrices 28-12-2020
- Essential Math for Data Science - Introduction to Matrices and the Matrix Product 16-12-2020
- Essential Math for Data Science: Scalars and Vectors 10-12-2020
- Essential Math for Data Science: Information Theory 26-11-2020
- Essential Math for Data Science: The Poisson Distribution 24-11-2020
- Essential Math for Data Science: Probability Density and Probability Mass Functions 12-11-2020
- Essential Math for Data Science: Integrals And Area Under The Curve 05-11-2020
- Essential Math for Data Science: New Chapters 03-08-2020
- Essential Math for Data Science 28-02-2020
- Deep Learning Book Series 3.4 and 3.5 Marginal and Conditional Probability 17-07-2019
- Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions 01-05-2019
- Preprocessing for deep learning: from covariance matrix to image whitening 27-08-2018
- Deep Learning Book Series · 2.12 Example Principal Components Analysis 26-03-2018
- Deep Learning Book Series · 2.11 The determinant 26-03-2018
- Deep Learning Book Series · 2.10 The Trace Operator 26-03-2018
- Deep Learning Book Series · 2.9 The Moore Penrose Pseudoinverse 26-03-2018
- Deep Learning Book Series · 2.8 Singular Value Decomposition 26-03-2018
- Deep Learning Book Series · 2.7 Eigendecomposition 26-03-2018
- Deep Learning Book Series · 2.6 Special Kinds of Matrices and Vectors 26-03-2018
- Deep Learning Book Series · 2.5 Norms 26-03-2018
- Deep Learning Book Series · 2.4 Linear Dependence and Span 26-03-2018
- Deep Learning Book Series · 2.3 Identity and Inverse Matrices 26-03-2018
- Deep Learning Book Series · 2.2 Multiplying Matrices and Vectors 26-03-2018
- Deep Learning Book Series · 2.1 Scalars Vectors Matrices and Tensors 26-03-2018
Numpy
- Essential Math for Data Science: Visual Introduction to Singular Value Decomposition (SVD) 17-04-2021
- Essential Math for Data Science: Eigenvectors and application to PCA 23-02-2021
- Essential Math for Data Science: Basis and Change of Basis 01-02-2021
- Essential Math for Data Science: Introduction to Systems of Linear Equations 13-01-2021
- Essential Math for Data Science: Linear Transformation with Matrices 28-12-2020
- Essential Math for Data Science - Introduction to Matrices and the Matrix Product 16-12-2020
- Essential Math for Data Science: Scalars and Vectors 10-12-2020
- Essential Math for Data Science: Information Theory 26-11-2020
- Essential Math for Data Science: The Poisson Distribution 24-11-2020
- Essential Math for Data Science: Probability Density and Probability Mass Functions 12-11-2020
- Essential Math for Data Science: Integrals And Area Under The Curve 05-11-2020
- Essential Math for Data Science: New Chapters 03-08-2020
- Essential Math for Data Science 28-02-2020
- Deep Learning Book Series 3.4 and 3.5 Marginal and Conditional Probability 17-07-2019
- Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions 01-05-2019
- Preprocessing for deep learning: from covariance matrix to image whitening 27-08-2018
- Deep Learning Book Series · 2.12 Example Principal Components Analysis 26-03-2018
- Deep Learning Book Series · 2.11 The determinant 26-03-2018
- Deep Learning Book Series · 2.10 The Trace Operator 26-03-2018
- Deep Learning Book Series · 2.9 The Moore Penrose Pseudoinverse 26-03-2018
- Deep Learning Book Series · 2.8 Singular Value Decomposition 26-03-2018
- Deep Learning Book Series · 2.7 Eigendecomposition 26-03-2018
- Deep Learning Book Series · 2.6 Special Kinds of Matrices and Vectors 26-03-2018
- Deep Learning Book Series · 2.5 Norms 26-03-2018
- Deep Learning Book Series · 2.4 Linear Dependence and Span 26-03-2018
- Deep Learning Book Series · 2.3 Identity and Inverse Matrices 26-03-2018
- Deep Learning Book Series · 2.2 Multiplying Matrices and Vectors 26-03-2018
- Deep Learning Book Series · 2.1 Scalars Vectors Matrices and Tensors 26-03-2018
Deep-learning-book
- Deep Learning Book Series 3.4 and 3.5 Marginal and Conditional Probability 17-07-2019
- Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions 01-05-2019
- Deep Learning Book Series · 2.12 Example Principal Components Analysis 26-03-2018
- Deep Learning Book Series · 2.11 The determinant 26-03-2018
- Deep Learning Book Series · 2.10 The Trace Operator 26-03-2018
- Deep Learning Book Series · 2.9 The Moore Penrose Pseudoinverse 26-03-2018
- Deep Learning Book Series · 2.8 Singular Value Decomposition 26-03-2018
- Deep Learning Book Series · 2.7 Eigendecomposition 26-03-2018
- Deep Learning Book Series · 2.6 Special Kinds of Matrices and Vectors 26-03-2018
- Deep Learning Book Series · 2.5 Norms 26-03-2018
- Deep Learning Book Series · 2.4 Linear Dependence and Span 26-03-2018
- Deep Learning Book Series · 2.3 Identity and Inverse Matrices 26-03-2018
- Deep Learning Book Series · 2.2 Multiplying Matrices and Vectors 26-03-2018
- Deep Learning Book Series · 2.1 Scalars Vectors Matrices and Tensors 26-03-2018
Computer-vision
Probability
- Deep Learning Book Series 3.4 and 3.5 Marginal and Conditional Probability 17-07-2019
- Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions 01-05-2019
Essential-math
- Essential Math for Data Science: Visual Introduction to Singular Value Decomposition (SVD) 17-04-2021
- Essential Math for Data Science: Eigenvectors and application to PCA 23-02-2021
- Essential Math for Data Science: Basis and Change of Basis 01-02-2021
- Essential Math for Data Science: Introduction to Systems of Linear Equations 13-01-2021
- Essential Math for Data Science: Linear Transformation with Matrices 28-12-2020
- Essential Math for Data Science - Introduction to Matrices and the Matrix Product 16-12-2020
- Essential Math for Data Science: Scalars and Vectors 10-12-2020
- Essential Math for Data Science: Information Theory 26-11-2020
- Essential Math for Data Science: The Poisson Distribution 24-11-2020
- Essential Math for Data Science: Probability Density and Probability Mass Functions 12-11-2020
- Essential Math for Data Science: Integrals And Area Under The Curve 05-11-2020
- Essential Math for Data Science: New Chapters 03-08-2020
- Essential Math for Data Science 28-02-2020