A feedforward neural network (FNN) is a simple type of artificial neural network where information flows in one direction, from the input layer to the output layer, through one or more hidden layers. It’s the most basic form of a neural network.
In FNNs, neurons in each layer are connected to all neurons in the next layer, forming a dense structure. The network learns by adjusting the weights of these connections during training to minimize prediction errors.
Feedforward networks are commonly used for tasks like image classification and regression. Despite their simplicity, they form the foundation for more complex models like CNNs and RNNs.