Ein Feedforward Neuronales Netzwerk (FNN) is a fundamental architecture in the Bereich der künstlichen Intelligenz verwendet wird, particularly within maschinellem Lernen. This type of neural network consists of interconnected nodes, or neurons, arranged in layers: an Eingabeschicht, one or more hidden layers, and an output layer. The primary characteristic of a feedforward network is that information flows in one direction—from the input layer, through the hidden layers, and finally to the output layer—without any cyclical connections.
In an FNN, each neuron in one layer is connected to every neuron in the subsequent layer, creating a dense network of connections. These connections are weighted, and each neuron applies an activation function to its input, which determines whether it will produce an output and, if so, what that output will be. Common Aktivierungsfunktionen used in FNNs include the sigmoid function, hyperbolic tangent (tanh), and the Rectified Linear Unit (ReLU).
Training a feedforward neural network typically involves a process called backpropagation, where the network adjusts its weights based on the error of its predictions compared to the actual outcomes. This Iterativer Prozess helps the network learn patterns within the training data, enabling it to make accurate predictions or classifications on unseen data.
Feedforward Neural Networks are widely used in various applications, including image recognition, natural language processing, and predictive analytics, due to their simplicity and effectiveness in handling structured data. While they are not as complex as other architectures like Konvolutionale Neuronale Netze (CNNs) or Recurrent Neural Networks (RNNs), they serve as the foundation for understanding deeper neural network architectures.