Output Layer Explained — Logits, Softmax, Cross-Entropy, and Why They Work Together

This article explains how neural networks generate meaningful predictions through probabilities, detailing the role of logits, softmax, and cross-entropy. It discusses how these components transform raw outputs into interpretable probability distributions and how the loss function measures accuracy, ensuring numerical stability.