What is the primary benefit of using an Activation Function in neural networks?

The primary benefit of using an Activation Function in neural networks is that it introduces non-linearity to the model. This non-linearity is crucial because most real-world data is non-linear in nature. If a neural network only employed linear transformations, it would be limited in its ability to model complex relationships within the data. By applying an activation function, such as sigmoid, tanh, or ReLU (Rectified Linear Unit), the network can make decisions that are not merely based on linear combinations of inputs.

Non-linearity allows the neural network to learn more complex patterns and relationships that better reflect how data operates in real scenarios. It enables the model to capture intricate features and interactions in the data, enhancing its expressiveness and ability to generalize from training data to unseen data. Thus, the incorporation of activation functions is a fundamental aspect of designing effective neural networks.