Fehlerrückführung

Fehlerrückführung, often translated as error feedback or error backpropagation, is a fundamental concept in the training of artificial neural networks. It describes the process by which the error or loss incurred by a neural network's prediction is propagated backward through the network's layers. This backward propagation allows the network to adjust its internal parameters, specifically the weights and biases, to minimize future errors.

The process begins with a forward pass, where input data is fed through the network to produce an output. This output is then compared to the desired target output, and the difference is quantified as an error. Fehlerrückführung takes this error and uses calculus, specifically the chain rule, to calculate the gradient of the error with respect to each weight and bias in the network. These gradients indicate the direction and magnitude of change needed for each parameter to reduce the overall error.

Once the gradients are computed, an optimization algorithm, most commonly gradient descent or one of its variants, is employed. This algorithm updates the weights and biases by subtracting a fraction of their respective gradients, scaled by a learning rate. This iterative process of forward pass, error calculation, backward propagation of error, and parameter update is repeated for many training examples and epochs until the network achieves a satisfactory level of accuracy. Fehlerrückführung is thus essential for enabling neural networks to learn from data and improve their performance over time.