Another way to understand cross-entropy is to think of it as a measure of surprise. When the predicted label differs greatly from the true label, the cross-entropy loss will be high, indicating a high level of surprise. Conversely, when the predicted label matches the true label, the cross-entropy loss will be low, implying little surprise. Therefore, by minimizing cross-entropy, the neural network learns to make more confident and accurate predictions.

Cross-entropy is a commonly used loss function in neural networks, particularly in classification tasks. It measures the dissimilarity between the predicted probability distribution and the true distribution of the labels. By minimizing the cross-entropy loss, the neural network aims to accurately classify the input data. This can be achieved through techniques such as backpropagation and gradient descent, which update the network's weights and biases based on the calculated gradients.

In addition to training neural networks, cross-entropy is also utilized in other areas of machine learning, such as evaluating the performance of models and comparing different probability distributions. In summary, cross-entropy serves as an essential tool in optimizing neural networks and enhancing their predictive capabilities.