Given a finite training set, machine learning models must rely on certain assumptions to achieve human-level performance. These assumptions, known as inductive biases, encode preferences for solutions with specific properties that often reflect how humans think about the world. For example, a deep multilayer perceptron (MLP) has an inductive bias towards building up a complicated function through the composition of simpler functions. The necessity of these biases stems from the 'no free lunch' theorem, which dictates that algorithms must make assumptions to generalize effectively.