What is usually defined when training neural networks?

When training neural networks, the performance function—also known as the loss function or cost function—plays a crucial role. This function quantifies how well the neural network’s predictions match the actual data. The performance function guides the learning process by providing feedback on how the model's predictions deviate from the expected outcomes. During training, the network adjusts its parameters to minimize this performance function, which effectively improves accuracy and efficiency.

The importance of the performance function cannot be overstated; it serves as the objective that the training algorithm seeks to optimize. By calculating the loss, we can determine how to adjust the weights of the network through techniques like gradient descent. The goal is to achieve the lowest possible value of the performance function, indicating that the model has learned to predict outcomes effectively based on the input data.

While concepts like a feedback loop, data validation process, and user interface are relevant to the broader context of machine learning and user interaction, they do not directly define the core mathematical mechanism that governs how the neural network learns from data during training.