AI Practitioner

What is Softmax? Softmax  is a mathematical function that transforms a vector of real-valued scores (logits) into a probability distribution over predicted output classes. It is commonly used in the  output layer of classification models , especially in  multi-class classification  problems. Mathematical Definition Given a vector of real numbers $z=[z_1,z_2,...,z_K]$, the softmax function outputs a vector   $\sigma(z)$ where: $\sigma(Z_i)=\frac{e^{z_i}}{\sum_{j=1}^{K}e^{z_j}} \text{(for i=1, ..., K)}$ Each element $\sigma(z_i)\in (0,1)$ and the elements sum to 1: $\sum_{i=0}^{K}\sigma(z_i)=1$. Why Use Softmax? It converts  raw scores (logits)  into probabilities. It helps the model  assign confidence  to predictions. It is  differentiable , enabling gradient-based optimization during training. Impact on Model Performance Classification Accuracy In combination with the  cross-entropy loss , softmax all...