Feedforward neural networks (FNNs) are the most basic type of neural network, where the data flows in one direction: from the input layer, through hidden layers, and to the output layer. There are no cycles or loops in a feedforward network, and each input is processed independently. This type of network is typically used for tasks like classification or regression.
Recurrent neural networks (RNNs), on the other hand, have loops within the network that allow information to be passed from one step to the next. This recurrent connection allows the network to process sequences of data, making RNNs ideal for tasks like speech recognition, language modeling, and time-series forecasting.
The key difference is that FNNs process inputs as independent instances, while RNNs are designed to capture temporal dependencies and relationships within sequences.