Forward March, Backward Blame

In one training step of a neural network, what does the forward pass actually do?

Applies the optimizer's update rule to adjust every weight

Passes the input through the network's layers to produce predictions and compute the loss

Propagates the loss backward through the layers to compute gradients for each weight

Randomly reinitializes the weights before each epoch