Forward, Loss, Backprop: The Loop

The Forward, Loss, Backprop loop is the core training process for a neural network, where a forward pass makes a prediction, a loss function calculates how wrong it is, and backpropagation computes gradients to update the model's weights, reducing error over many iterations to improve future predictions.

1. Forward: compute predictions from inputs via layers and activations.
2. Loss: compare predictions to targets.
3. Backward: compute gradients of loss w.r.t. each parameter (backpropagation).
4. Update: adjust parameters with gradient descent (or a fancier optimizer).