Activation Functions In Deep Neural Networks
Activation Functions in Deep Neural Networks Activation functions are essential components in deep neural networks, introducing non-linearity into the models and enabling them to learn complex patterns from data. Here's a detailed overview of various activation functions used in deep learning, along with their characteristics, advantages, and applications: 1. Sigmoid Function Definition : The sigmoid function transforms input values into a range between 0 and 1, given by: σ ( x ) = 1 1 + e − x \sigma(x) = \frac{1}{1 + e^{-x}} σ ( x ) = 1 + e − x 1 Characteristics : Range : (0, 1) Pros : Useful for binary classification tasks, where outputs can be interpreted as probabilities. Cons : Vanishing Gradient : For extreme values, the gradient approaches zero, which can slow down learning. Not Zero-Centered : Outputs are always positive, which may lead to inefficiencies in learning. Use Cases : Commonly used in the output layer for binary classification problems. 2. Tanh (Hyperbolic Ta...