In neural networks, which method enables a developer to obtain the output and compare it with the real value to get the error?

In neural networks, back propagation is the method used to compute the gradient of the loss function with respect to the weights of the network. This is achieved by first performing forward propagation, where inputs are passed through the network to obtain the output. Once the output is generated, it can be compared to the real or expected values to calculate the error (or loss). The back propagation algorithm then uses this error to adjust the weights of the network in a way that minimizes the loss on future predictions.

This process is crucial for training neural networks, as it guides the optimization of the model during the learning phase by indicating how to change the weights to improve accuracy. Understanding this method is essential for any developer working with neural networks, as it integrates the key concepts of learning and error correction.