A weight update is the adjustment applied to a neural network’s parameters after each training step, computed by scaling the gradient produced by backpropagation by a learning rate and subtracting it from the current weights. Successive weight updates across many epochs move the network toward a configuration that minimises the training loss. The update rule — plain gradient descent, momentum, or an adaptive optimiser such as Adam — determines convergence speed and stability.