The feed-forward neural network was the first and simplest type of artificial neural network devised. It contains multiple neurons (nodes) arranged in layers. Nodes from adjacent layers have connections or edges between them. All these connections have weights associated with them.
An example of a feed-forward neural network is shown in Figure 7:
Figure 7: An example feed-forward neural network
In a feed-forward network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any), and to the output nodes. There are no cycles or loops in the network (this property of feed-forward networks is different from recurrent neural networks, in which the connections between nodes form a cycle).
