Vorwärtsdurchgang

Vorwärtsdurchgang, also known as forward pass, is a fundamental concept in neural networks and other computational models. It refers to the process of calculating the output of a network by feeding input data through its layers in a sequential manner. Starting from the input layer, the data is processed by each subsequent layer, with the output of one layer becoming the input for the next. This flow continues until the final output layer is reached.

In a typical neural network, each layer consists of neurons that perform mathematical operations on their inputs. These operations usually involve a weighted sum of the inputs followed by an activation function. The weights and biases, which are learned parameters of the network, determine how the input data is transformed at each step. The forward pass is crucial for both training and inference. During training, it's used to compute the network's predictions, which are then compared to the actual target values to calculate the error. This error is subsequently used to update the network's weights and biases through a process called backpropagation. In inference, the forward pass simply takes new input data and produces the network's prediction without any weight updates. The efficiency and accuracy of the forward pass are critical for the overall performance of the model.