This is a common yet one of the most important deep learning interview questions and answers for experienced professionals, don't miss this one. In the theory of artificial neural networks, the universal approximation theorem states that a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets of Rⁿ, under mild assumptions on the activation function. It does state that simple neural networks can represent a wide variety of interesting functions when given appropriate parameters; however, it does not touch upon the algorithmic learnability of those parameters. Examples of universal approximators are – Kernel SVM Neural Networks Boosted Decision Trees All of these methods can approximate any function.