Feedforward Neural Network

Definition - What does Feedforward Neural Network mean?

The feedforward neural network is a specific type of early artificial neural network known for its simplicity of design. The feedforward neural network has an input layer, hidden layers and an output layer. Information always travels in one direction – from the input layer to the output layer – and never goes backward.

Techopedia explains Feedforward Neural Network

The feedforward neural network, as a primary example of neural network design, has a limited architecture. Signals go from an input layer to additional layers. Some examples of feedforward designs are even simpler. For example, a single-layer perceptron model has only one layer, with a feedforward signal moving from a layer to an individual node. Multi-layer perceptron models, with more layers, are also feedforward.

In the days since scientists devised the first artificial neural networks, the technology world has made all sorts of progress in building more sophisticated models. There are recurrent neural networks and other designs that contain loops or cycles. There are models that involve backpropagation, where the machine learning system essentially optimizes by sending data back through a system. The feedforward neural network does not involve any of this type of design, and so it is a unique type of system that is good for learning these designs for the first time.

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