Loss Functions

Loss functions measure how well the network's predictions match the actual values. They quantify the difference between the predicted output and the true output for a given input. The goal during training is to minimize this loss, guiding the network to adjust its internal parameters (weights and biases) to make more accurate predictions. Different loss functions are suitable for different types of problems, such as regression or classification.

Visit the following resources to learn more:

• @article@Loss Functions and Their Use In Neural Networks
• @article@What is Loss Function? | IBM
• @video@Loss in a Neural Network explained