In a neural network, the ______________ function measures the difference between the actual output and the predicted output.

In a neural network, the loss function measures the difference between the actual output and the predicted output. The loss function, also known as the cost function or error function, quantifies how well the neural network's predictions align with the true labels of the data. Common loss functions include Mean Squared Error (MSE) for regression tasks and Cross-Entropy Loss for classification tasks. The objective during training is to minimize this loss function by adjusting the network's weights and biases through optimization algorithms such as stochastic gradient descent. A lower loss indicates a better model performance, as it implies that the predicted values are closer to the true values. Understanding and selecting the appropriate loss function is crucial for the efficiency and effectiveness of the training process in neural networks.