Essential Math for Data Science: Visual Introduction to Singular Value Decomposition (SVD)
Transformation of the unit circle and basis vectors. ⥈ ⥈ ⥈Essential Math for Data Science: Eigenvectors and application to PCA
The variance of the data in the direction of the vector $\vu$ (red) is larger than in the direction of the vector $\vv$ (green). ⥈ ⥈ ⥈Essential Math for Data Science: Basis and Change of Basis
The basis vectors in the Cartesian plane. ⥈ ⥈ ⥈Essential Math for Data Science: Introduction to Systems of Linear Equations
Plot of the equation $y=2x+1$. ⥈ ⥈ ⥈Essential Math for Data Science: Linear Transformation with Matrices
Each point corresponds to the combination of x and y values. ⥈ ⥈ ⥈Essential Math for Data Science - Introduction to Matrices and the Matrix Product
Steps of the product between a matrix and a vector. ⥈ ⥈ ⥈Essential Math for Data Science: Scalars and Vectors
A geometric vector running from $A$ to $B$. ⥈ ⥈ ⥈Essential Math for Data Science: Information Theory
The cross-entropy as a measure of difference between two distributions ⥈ ⥈ ⥈Essential Math for Data Science: The Poisson Distribution
Emails received by Sarah in one-hour intervals for the last 100 hours. ⥈ ⥈ ⥈Essential Math for Data Science: Probability Density and Probability Mass Functions
The probability to draw a number between 0 and 0.2 is the highlighted area under the curve. ⥈ ⥈ ⥈Essential Math for Data Science: Integrals And Area Under The Curve
Area under the curve. ⥈ ⥈ ⥈Essential Math for Data Science: New Chapters
L1 Regularization ⥈ ⥈ ⥈Essential Math for Data Science
SVD Geometry ⥈ ⥈ ⥈Deep Learning Book Series 3.4 and 3.5 Marginal and Conditional Probability
The sum rule allows to calculate marginal probability from joint probability. ⥈ ⥈ ⥈Deep Learning Book Series 3.1 to 3.3 Probability Mass and Density Functions
Probability density function and area under the curve between 0.5 and 0.7. ⥈ ⥈ ⥈Preprocessing for deep learning: from covariance matrix to image whitening
The left plot shows correlated data. For instance, if you take a data point with a big $x$ value, chances are that $y$ will also be quite big. Now take all data points and do a rotation (maybe around 45 degrees counterclockwise): the new data (plotted on the right) is not correlated anymore. ⥈ ⥈ ⥈Deep Learning Book Series · 2.12 Example Principal Components Analysis
Projection of the data point: the line direction is the one with the largest variance ⥈ ⥈ ⥈Deep Learning Book Series · 2.11 The determinant
The determinant of a matrix can tell you a lot of things about the transformation associated with this matrix ⥈ ⥈ ⥈Deep Learning Book Series · 2.10 The Trace Operator
The trace of matrix ⥈ ⥈ ⥈Deep Learning Book Series · 2.9 The Moore Penrose Pseudoinverse
There is more equations (3) than unknowns (2) so this is an overdetermined system of equations ⥈ ⥈ ⥈Deep Learning Book Series · 2.8 Singular Value Decomposition
The unit circle and its transformation by a matrix ⥈ ⥈ ⥈Deep Learning Book Series · 2.7 Eigendecomposition
The unit circle and its transformation by the matrix A. The vectors are the eigenvectors of A. ⥈ ⥈ ⥈Deep Learning Book Series · 2.6 Special Kinds of Matrices and Vectors
Example of diagonal and symmetric matrices ⥈ ⥈ ⥈Deep Learning Book Series · 2.5 Norms
The squared L2 norm ⥈ ⥈ ⥈Deep Learning Book Series · 2.4 Linear Dependence and Span
A system of equations has no solution, 1 solution or an infinite number of solutions ⥈ ⥈ ⥈Deep Learning Book Series · 2.3 Identity and Inverse Matrices
A 3 by 3 identity matrix ⥈ ⥈ ⥈Deep Learning Book Series · 2.2 Multiplying Matrices and Vectors
The dot product between a matrix and a vector ⥈ ⥈ ⥈Deep Learning Book Series · 2.1 Scalars Vectors Matrices and Tensors
Difference between a scalar, a vector, a matrix and a tensor ⥈ ⥈ ⥈Deep Learning Book Series · Introduction
The Deep Learning Book - Goodfellow, I., Bengio, Y., and Courville, A. (2016) ⥈ ⥈ ⥈