What is the softmax function and how is it interpreted in machine learning?

Can you explain what the softmax function is?

The softmax function is a mathematical function that converts a vector of numbers into probabilities. It is often used in machine learning for multi-class classification.

How does the softmax function work mathematically?

Mathematically, the softmax function takes a vector z and outputs a vector of the same length, where each element is calculated as exp(z_i) / sum(exp(z_j)) for all j. This ensures that the output values sum to 1.

What are some applications of the softmax function?

Softmax is commonly used in neural networks, especially in the final layer for classification tasks, such as image recognition and natural language processing.

Can you give an example of how softmax is used in a neural network?

Sure! In a neural network for image classification, the final layer might use softmax to output probabilities for each class, allowing the model to predict which class the input image belongs to.