With the rise of the importance of automatic differentiation and gradient descent in various optimization fields, differentiation has become an important construct to formalize in terms of programming language semantics. However, many contemporary models of languages have difficulty handling points of discontinuity introduced by conditionals as derivatives are not well defined at such points. We offer a solution to this undefined behavior by defining a denotational semantics based on distribution theory.