Description: The Exponential Linear Unit (ELU) is an activation function used in neural networks that aims to improve learning characteristics by allowing negative values. Unlike traditional activation functions like the Rectified Linear Unit (ReLU), which only permits positive values, ELU introduces a negative component that helps mitigate the ‘dying’ neuron problem, where some neurons stop learning entirely. ELU is mathematically defined as f(x) = x if x > 0, and f(x) = α * (exp(x) – 1) if x ≤ 0, where α is a parameter that controls the saturation of the function for negative values. This feature allows ELU to maintain a mean close to zero, which can accelerate learning and improve convergence compared to other activation functions. Additionally, ELU is differentiable at all points, which is crucial for training neural networks through backpropagation. Its ability to handle both positive and negative values makes it an attractive option for deep network architectures, where diversity in activations can be beneficial for representing complex data.
