ELU (Exponential Linear Unit)

Information

• The ELU (Exponential Linear Unit) is a type of activation function commonly used in artificial neural networks.
• It is similar to the ReLU activation function, but with some modifications to handle negative inputs better.
• The ELU function is defined as f(x) = x for x ≥ 0 and f(x) = α(e^x - 1) for x < 0, where α is a hyperparameter that controls the output value for negative inputs.
• The advantage of ELU over ReLU is that it can handle negative input values without being "dead", meaning that it can still produce an output and allow gradients to flow through the network during backpropagation.
• ELU also tends to produce more robust and accurate models, especially when used in deep neural networks.
• One potential disadvantage of ELU is that it can be computationally more expensive than ReLU, especially when dealing with large datasets and complex models.
• Despite this, ELU remains a popular choice for activation functions in many neural network architectures due to its improved performance compared to other commonly used activation functions.