feedforwardnetwerken
Feedforward neural networks, often abbreviated as FNNs, represent a fundamental type of artificial neural network. Their defining characteristic is the unidirectional flow of information, meaning data moves forward from the input layer, through one or more hidden layers, and finally to the output layer. There are no cycles or loops in the network structure, distinguishing them from recurrent neural networks.
Each neuron in a feedforward network receives inputs from neurons in the preceding layer, processes these inputs
Feedforward networks are widely used for various tasks, including classification, regression, and pattern recognition. Their simplicity