{"id":6753,"date":"2018-02-02T14:24:03","date_gmt":"2018-02-02T08:54:03","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=6753"},"modified":"2025-07-27T19:17:32","modified_gmt":"2025-07-27T13:47:32","slug":"recurrent-neural-networks","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/recurrent-neural-networks\/","title":{"rendered":"What are Recurrent Neural Networks? An Ultimate Guide for Newbies!"},"content":{"rendered":"

Apple\u2019s Siri and Amazon\u2019s Alexa have one thing in common apart from being personal assistants – they both use Recurrent Neural Networks to understand human speech and generate replies. Not only this, almost every company is using Recurrent Neural Networks. And therefore to explain RNN in simple terms, DataFlair brings the latest article on Recurrent Neural Network by discussing it with data scientists and machine learning experts.<\/p>\n

Here, we will discuss the most important type of machine learning algorithm<\/strong><\/em><\/a> – Recurrent Neural Network (RNN). These type of algorithms help you out the most when you translate one language into another. We will understand background, working and various applications of RNN. So, let’s start the tutorial with the basic introduction to Recurrent Neural Networks.<\/p>\n

What are Recurrent Neural Networks?<\/h3>\n

A Recurrent Neural Network is a type of Neural Network where there exists a connection between the nodes along a temporal sequence. This connection is that of a directed graph. By temporal, we mean data that transitions with time.\u00a0<\/em><\/p>\n

RNN Example –<\/strong> <\/em>time-series data involving prices of stock prices that change with time, sensor readings, medical records, etc. These recurrent neural networks use their internal state or memory to process the sequence of input data. Such input is dependent upon the previous input. Therefore, there is a connection between the input sequences. Therefore, we use them in areas like natural language processing<\/strong> <\/em><\/a>and speech recognition.<\/p>\n

Why RNN?<\/h4>\n

Traditional neural networks lack the ability to address future inputs based on the ones in the past. For example, a traditional neural network cannot predict the next word in the sequence based on the previous sequences. However, a recurrent neural network (RNN) most definitely can. Recurrent Neural networks, as the name suggests are recurring. Therefore, they execute in loops allowing the information to persist.<\/p>\n

\"RNN<\/a><\/p>\n

In the above diagram, we have a neural network that takes the input xt and gives use the output ht. Therefore, the information is passed from one step to the successive step. This recurrent neural network, when unfolded can be considered to be copies of the same network that passes information to the next state.<\/p>\n

RNNs allow us to perform modeling over a sequence or a chain of vectors. These sequences can be either input, output or even both. Therefore, we can conclude that neural networks are related to lists or sequences. So, whenever you have data of sequential nature, you should apply recurrent neural networks.<\/p>\n

Don’t forget to check our leading blog on Artificial Neural Networks<\/a><\/strong><\/em><\/p>\n

How do Recurrent Neural Networks Work?<\/h3>\n

In the traditional neural networks, there is a hidden layer with its own set of weights and biases. Let us assume this weight and bias to be w1 and b1 for weight and bias 1 respectively. Similarly, we will have w2,b2 and w3,b3 for the third layer. These layers are also independent of one another, meaning that they do not memorize the previous output. Suppose there is a deeper network with one input layer, three hidden layers, and one output layer.<\/p>\n

\"Rnn<\/a><\/p>\n

The recurrent neural network will perform the following –<\/p>\n