Feedforward Neural Network

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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Margaret Rouse

Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical, business audience. Over the past twenty years her explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her…