An

We have a function:

\begin{equation} |x|+|y|\frac{dy}{dx} = \sin \left(\frac{x}{n}\right) \end{equation}

We are to attempt to express the solution analytically and also approximate them.

To develop a basic approximate solution, we will leverage a recursive simulation approach.

We first set a constant \(N\) which in the \(N\) value which we will eventually vary.

N = 0.5

We can get some values by stepping through \(x\) and \(y\) through which we can then figure \(\frac{dy}{dx}\), namely, how the function evolves.

Generative Classifier

A Generative Classifier builds a good model of a class, and use that to assign how “class-y” is that image.

For instance, to categorize cats vs. dogs, we build a cat model and dog model. To classify, then, we see if a particular image is more “cat-y” or “dog-y”.

Discriminative Classifier

A Discriminative Classifier observes the differences between two classes, instead of trying to model each one.

A Differential Equation is a function-valued algebreic equation whose unknown is an entire function \(y(x)\), where the equation involves a combination of derivatives $y(x), y’(x), …$.

See Differential Equations Index

and Uniqueness and Existance