Translations:Neural Networks/24/en

1. Defining a loss function — a measure of how far the network's predictions are from the true targets (see Loss Functions).
2. Forward pass — computing the output of the network for a given input by propagating values layer by layer.
3. Backward pass (backpropagation) — computing the gradient of the loss with respect to every weight by applying the chain rule in reverse through the network (see Backpropagation).
4. Parameter update — adjusting the weights using an optimisation algorithm such as Gradient Descent or one of its variants.
5. Iteration — repeating steps 2–4 over many passes (epochs) through the training data.