Difference Between Feed-Forward Neural Networks and Recurrent Neural Networks

Pre-requisites: Artificial Neural Networks and its Applications 

Neural networks are artificial systems that were inspired by biological neural networks. These systems learn to perform tasks by being exposed to various datasets and examples without any task-specific rules.

In this article, we will see the difference between Feed-Forward Neural Networks and Recurrent Neural Networks.

Feed-Forward Neural Networks

The feedforward neural network is one of the most basic artificial neural networks. In this ANN, the data or the input provided travels in a single direction. It enters into the ANN through the input layer and exits through the output layer while hidden layers may or may not exist. So the feedforward neural network has a front-propagated wave only and usually does not have backpropagation

Recurrent Neural Networks

The Recurrent Neural Network saves the output of a layer and feeds this output back to the input to better predict the outcome of the layer. The first layer in the RNN is quite similar to the feed-forward neural network and the recurrent neural network starts once the output of the first layer is computed. After this layer, each unit will remember some information from the previous step so that it can act as a memory cell in performing computation

Feed-Forward Neural Networks vs Recurrent Neural Networks

The below table provides a quick comparison between feed-forward neural networks and recurrent neural Networks

Comparison Attribute	Feed-forward Neural Networks	Recurrent Neural Networks
Signal flow direction	Forward only	Bidirectional
Delay introduced	No	Yes
Complexity	Low	High
Neuron independence in the same layer	Yes	No
Speed	High	slow
Commonly used for	Pattern recognition, speech recognition, and character recognition	Language translation, speech-to-text conversion, and robotic control

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