Im Kontext von künstliche Intelligenz and neuronale Netze, verborgene Einheiten refer to the neurons located within the hidden layers of a neuronaler Netzwerkarchitektur. These units are called ‘hidden’ because they are not directly exposed to the input or output layers; instead, they serve as intermediary processing nodes that contribute to the network’s ability to learn complex patterns and representations from the input data.
Verborgene Einheiten arbeiten, indem sie Aktivierungsfunktionen to the weighted sum of their inputs, which allows them to capture non-linear relationships within the data. The outputs from these hidden units are then passed on to subsequent layers, eventually leading to the final output layer of the network. The number of hidden units, as well as the number of hidden layers, can significantly influence the capacity and performance of a neural network.
Common activation functions used by hidden units include the Rectified Linear Unit (ReLU), Sigmoid, and Tanh functions. The choice of Aktivierungsfunktion can affect how well the network learns from the training data and its ability to generalize to new, unseen data.
Overall, hidden units play a crucial role in enabling neural networks to perform tasks such as image recognition, der Verarbeitung natürlicher Sprache, and many other applications where learning from data is essential.