What is softmaxxing?

Softmax function is a non-linear activation function that is widely used in machine learning algorithms. It is a function that can convert any vector of real numbers into a probability distribution. The softmax function is commonly used in classification problems to convert the output of a model into probabilities that can be used to make predictions.

The softmax function works by taking an input vector of arbitrary real numbers and mapping them to a new vector of values between 0 and 1 that add up to 1. The output of the softmax function is a probability distribution over the classes, where each element of the vector represents the probability of the input belonging to that particular class.

The softmax function can be used for both binary and multi-class classification problems. In binary classification problems, the softmax function reduces to the sigmoid function. In multi-class classification problems, the softmax function is used to assign probabilities to each of the possible classes.

Softmax function is widely used in neural network models for image classification, natural language processing, and speech recognition tasks. It is also used in machine learning algorithms such as logistic regression, linear discriminant analysis, and multinomial regression.