Description: Non-linearity is a fundamental property in the realm of mathematical functions and, particularly, in the context of neural networks. It refers to the inability of a function to satisfy the principle of superposition, meaning that the output of the function is not simply the sum of its inputs. In the context of neural networks, non-linearity is introduced through activation functions such as ReLU (Rectified Linear Unit), sigmoid, or hyperbolic tangent. These functions allow neural networks to model complex, non-linear relationships in data, which is crucial for various tasks across different domains, including image classification, natural language processing, and time series prediction. Without non-linearity, a neural network, no matter how many layers it has, would behave like a simple linear combination of its inputs, limiting its ability to learn complex patterns. Therefore, non-linearity is essential for the expressive power of neural networks, enabling them to learn and generalize from complex and varied data.
