what is softmax function

The softmax function is a mathematical function that converts a vector of real numbers into a probability distribution. It is commonly used in machine learning and deep learning algorithms for classification tasks. The softmax function takes an input vector and applies the exponential function to each element of the vector, then normalizes the resulting vector by dividing each element by the sum of all exponential values. This results in a vector of probabilities that add up to 1, where each element represents the probability of the input belonging to a particular class. The softmax function is defined as follows:

softmax(x_i) = e^(x_i) / sum(e^(x_j)) for all j in the input vector x.