"The Power of ReLU in Approximating Nonlinear Functions"

ReLU activation enables neural networks to approximate continuous nonlinear functions, such as Continuous PieceWise Linear (CPWL) and Continuous Curve (CC) functions, by allowing multiple units to contribute to the resulting function without interfering. The network architecture and number of hidden units play a crucial role in obtaining a good approximation result, with the appropriate number of hidden units being large enough to fit the data properly and small enough to avoid overfitting. Additionally, ReLU activations can be used for classification problems, and increasing the number of hidden layers with ReLU activation can change the network's approximation ability.