Modeling in Machine Learning
As AI engineers, we usually define each task or problem as a function.
For example, if we are developing a facial recognition system, our first step is to define the problem as a function F( X ) that maps input images to identifiers. But the question is how to know the F(X) formula?
In fact, it is not feasible to define F(X) using a formula or a set of inherent rules (I will explain why someday).
In general, instead of finding or defining the correct function F(X) , we try to find an approximation of F(X) . We call this approximation the hypothesis function , or simply H(X) .