# Deep Learning

## Laplace transformation

The Fourier series represents a periodic function as a descrete vectors. The Fourier transformation turns a time domain non-periodic function into a frequency domain continuous function. The Fourier series and transformation change a single time base $$t$$ into infinite frequency basis $$e^{inx}$$ or $$e^{iwx}$$. The function on infinite basis domain can be represented by a vector or a function of basis domain $$v_{n}$$ or $$f(w)$$. This is a coefficients of Fourier series or Fourier transformation.

## Lagrange dual problem and conjugate function

The optimization problem have two components that are objective function $$f_0 : \mathbb R ^n \rightarrow \mathbb R$$ and the constraints. The objective function and constraints keep in check each other and make balance at saddle point i.e. optimal point. The dual (Lagrange) problem of the optimal problem also solve the optimization problem by making low boundary. The dual problem can be explained as a conjugate function $$f^* = \sup (x^Ty-f(x))$$.

## Approximation

The purpose of approximation is finding optimal point $$x^*$$ i.e. $$\nabla F(x^*) = 0$$. We need a step/search direction $$\Delta x$$ and step size $$t$$. Taylor approximation has polynomial arguments that is a step and parameters of derivatives at the start point. The first degree of Taylor approximation has one adding term from start point $$(x_0, F(x_0))$$. The adding term $$\nabla F(x) \Delta x$$ is consistent with a parameter (gradient $$\nabla F(x)$$) and a argument (step $$\Delta x$$).

## Singular vector decomposition

Bases are the central idea of linear algebra. An invertable square matrix has eigenvectors. A symetric matrix has orthogonal eigenvectors with non-negative eigenvalues, i.e. positive semidefinite. A matrix has two types of singular vectors, left and right signular vectors, $$A=U\Sigma V^{T}$$. When we think the matrix $$A$$ is data points of rows $$A=U\Sigma V^{T}$$ like data table, The right singular vectors $$V$$ build bases, the sigular values $$\Sigma$$ are magnitude of the bases and the left singular values $$U$$ becomes new data points on new bases.

The meaning of $$A^{T}$$ Steady state equilibrium Graph Laplacian matrix $$A^{T}CA$$ Differential equation and Laplacian matrix Derivative is a graph without branch. Row space and column space are dual. $$A$$ and $$A^{T}$$ are dual. ref) Linear algebra and learning from data, Part IV, Gilbert Strang