Difference Between Feed-Forward Neural Networks and Recurrent Neural Networks Last Updated : 06 Jan, 2023 Comments Improve Suggest changes Like Article Like Report 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 NetworksThe 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 NetworksThe 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 NetworksThe below table provides a quick comparison between feed-forward neural networks and recurrent neural Networks Comparison AttributeFeed-forward Neural NetworksRecurrent Neural NetworksSignal flow directionForward onlyBidirectional Delay introduced NoYes ComplexityLowHighNeuron independence in the same layerYesNoSpeedHighslowCommonly used forPattern recognition, speech recognition, and character recognitionLanguage translation, speech-to-text conversion, and robotic control Comment More infoAdvertise with us Next Article Difference Between Feed-Forward Neural Networks and Recurrent Neural Networks K khurpaderushi143 Follow Improve Article Tags : Data Analysis Neural Network data mining Similar Reads Difference between Recursive and Recurrent Neural Network Recursive Neural Networks (RvNNs) and Recurrent Neural Networks (RNNs) are used for processing sequential data, yet they diverge in their structural approach. Let's understand the difference between this architecture in detail. What are Recursive Neural Networks (RvNNs)?Recursive Neural Networks are 2 min read Difference between Neural Network And Fuzzy Logic Neural Network: Neural network is an information processing system that is inspired by the way biological nervous systems such as brain process information. A neural network is composed of a large number of interconnected processing elements known as neurons which are used to solve problems. A neura 2 min read What is the Difference between a "Cell" and a "Layer" within Neural Networks? Answer: In neural networks, a "cell" refers to the basic processing unit within a recurrent neural network (RNN), such as a long short-term memory (LSTM) cell, while a "layer" is a structural component comprising interconnected neurons in the network architecture, including convolutional layers, den 1 min read Difference between a Neural Network and a Deep Learning System Since their inception in the late 1950s, Artificial Intelligence and Machine Learning have come a long way. These technologies have gotten quite complex and advanced in recent years. While technological advancements in the Data Science domain are commendable, they have resulted in a flood of termino 7 min read Bidirectional Recurrent Neural Network Recurrent Neural Networks (RNNs) are designed to handle sequential data such as speech, text and time series. Unlike traditional feedforward neural networks which process inputs as fixed-length vectors, RNNs can manage variable-length sequences by maintaining a hidden state that stores information f 7 min read Like