A neural network adjusts weights to reduce a loss function. The update rule uses the gradient $abla L$ and a learning rate $eta$ to decide how large each step should be.

More hidden layers and neurons increase model capacity, but they also raise the chance of overfitting if the training run is long and regularization stays weak.

The preview here is a teaching simulation, so it focuses on training tradeoffs instead of full numerical backpropagation.