Demystifying Softmax Loss: A Step-by-Step Derivation for Linear Classifiers

If you are diving into the mechanics of neural networks, you will inevitably encounter the backpropagation of the Softmax and Cross-Entropy loss. At first glance, the matrix calculus can feel a bit intimidating. However, once you break it down step-by-step using the chain rule, you will discover that the final gradients are incredibly elegant and intuitive.