Sigmoid Activation Function And Its Uses.
The sigmoid activation function is a mathematical function commonly used in artificial neural networks and machine learning models. It maps the input values to a range between 0 and 1, providing smooth and continuous output. The sigmoid function is defined as: σ(x) = 1 / (1 + e^(-x)) Where: x is the input to the function e is the base of the natural logarithm (approximately 2.71828) The sigmoid function has an S-shaped curve that increases from 0 to 1 as the input value increases. It has the desirable property of being differentiable, which is a valuable for training neural networks using gradient-based optimization algorithms like backpropagation. One of the main applications of the sigmoid function is in binary classification problems, where the goal is to assign inputs to one of two classes. The sigmoid function is commonly used as the activation function in the output layer of a neural network for this purpose. The output value, between 0 and 1, can be interpreted as the pro