zurückpropagiert

Zurückpropagiert, often translated as backpropagation, is a fundamental algorithm in the training of artificial neural networks. It is the primary method used to adjust the weights of a neural network in response to the error it makes during training. The process begins after the network has made a prediction for a given input. The difference between this prediction and the actual correct output, known as the error, is calculated.

This error is then propagated backward through the network, layer by layer. For each neuron, the algorithm determines how much that neuron contributed to the overall error. This contribution is quantified using calculus, specifically by calculating the gradient of the error function with respect to the neuron's weights and biases. The gradient indicates the direction and magnitude of the steepest increase in error.

Once these gradients are computed, the network's weights and biases are adjusted in the opposite direction of the gradient. This adjustment aims to minimize the error in subsequent predictions. The learning rate, a hyperparameter, controls the size of these adjustments. This iterative process of forward propagation, error calculation, and backpropagation is repeated many times over a large dataset, allowing the neural network to learn complex patterns and improve its performance.