A feedforward neural network (FNN) is a fundamental type of artificial neural network where information moves in one direction—from input nodes, through hidden layers, to output nodes. Unlike recurrent neural networks, FNNs do not have cycles or loops, making them simpler in structure. Each neuron in the network processes inputs and passes the output to the next layer, allowing for complex function approximation. FNNs are widely used in various applications, including image recognition, natural language processing, and more. Understanding FNNs is crucial for grasping more advanced neural network architectures, such as convolutional and recurrent networks.