What is Softmax

Softmax is an activation function commonly used in multi-class machine learning models, transforming a set of arbitrary real numbers into a probability distribution.

Mathematically, it is defined as follows:
Softmax(z_i) = e^{z_i} / sum(e^{z_j}) where z_i is the i-th element of the input vector and K is the total number of classes.

This function guarantees that the output values sum to 1, making it suitable for classification tasks like image recognition and natural language processing.

For example, in image recognition, Softmax converts the network's output into probabilities for each category, helping the model decide the class of the input image. In text classification, it determines the topic to which the text belongs.

Looking ahead, Softmax may be combined with advanced algorithms to improve classification accuracy and efficiency.

However, it has limitations, such as computational overhead with a large number of classes and sensitivity to variations in input data.

When using Softmax, ensure the input data is well-scaled to avoid numerical instability, especially with extreme values.