Universal Function Approximators refer to mathematical models or algorithms that can, theoretically, approximate any arbitrary function with sufficient complexity, given the right architecture and training. Neural networks are one of the most well-known universal function approximators. They are capable of modeling and approximating a wide range of functions, from simple linear relationships to highly complex, nonlinear mappings.
Universal Function Approximators like neural networks have numerous practical applications across fields such as image and speech recognition, natural language processing, reinforcement learning, and more. They can approximate functions that map input data to output data, making them versatile tools for solving real-world problems.