Softmax function and Misconception

Softmax is a mathematical function used to normalize the values between 0 and 1. In Deep Learning, Softmax is used as the activation function to normalize the output and scale each value in vector between 0 and 1. Softmax is used for classification tasks. At the last layer of the Network, an N-dimensional vector gets generated, one for each class in the Classification task. Softmax is used to normalize those weighted sum values between 0 and 1, and sum of them is equals to 1, that's why most people consider these values as Probabilities of classes but it is a Misconception we will discuss it in this article. Using this mathematical expression, we calculate normalized values for each class of data.