Feedforward-Netzwerk
Ein Feedforward-Netz ist eine grundlegende Art von künstlichem neuronaler Netzwerkarchitektur im maschinellen Lernen and artificial intelligence. In this model, information moves in one direction—from the input nodes, through hidden layers, and finally to the output nodes. Unlike rekurrente neuronale Netzwerke, feedforward networks do not have cycles or loops, meaning that data is processed in a straightforward manner without returning to previous layers.
The architecture typically consists of three main components: the input layer, one or more hidden layers, and the Ausgabeschicht. Each layer comprises nodes (or neurons) that perform computations based on the input they receive. The input layer receives raw data, such as images or text, and each connection between nodes has an associated weight that adjusts as the network learns.
Während des Trainingsprozesses verwendet das Netzwerk eine Methode namens backpropagation, which allows it to minimize the difference between its predicted output and the actual output. This is achieved by adjusting the weights of the connections based on the error calculated. As a result, the network improves its accuracy in making predictions over time.
Feedforward networks can be used for various tasks, including classification, regression, and function approximation. They are particularly effective for problems where the relationships between inputs and outputs are straightforward and do not require memory of past inputs, making them suitable for tasks like image recognition and basic Musterklassifikation.
In summary, a feedforward network is a simple yet powerful neural network architecture that lays the groundwork for more complex models in Deep Learning.