Recurrent Neural Network (RNN): Types and Applications

One of the most popular machine learning algorithms, neural networks outperforms other algorithms in both accuracy and speed. As a result, it is critical to have a thorough understanding of what a Neural Network is, how it is constructed, and what its reach and limitations are.

 

A recurrent neural network (RNN) is a type of artificial neural network that works with time series or sequential data. These deep learning algorithms are commonly used for ordinal or temporal problems like language translation, natural language processing (NLP), speech recognition, and image captioning; they are included in popular applications like Siri, voice search, and Google Translate. Recurrent neural networks, like feedforward and convolutional neural networks (CNNs), learn from training data.

 

 

What is a Recurrent Neural Network? 

 

Recurrent Neural Networks (RNN) are a type of Neural Network in which the previous step's output is fed as input to the current step. In traditional neural networks, all inputs and outputs are independent of one another; however, when predicting the next word of a sentence, the previous words are required, and thus the previous words must be remembered. Thus, RNN was born, which solved this problem with the help of a Hidden Layer. The Hidden state, which remembers some information about a sequence, is the main and most important feature of RNN.

 

RNNs have a "memory" that remembers everything that has been calculated. It employs the same parameters for each input because it performs the same task on all inputs or hidden layers to generate the output. Unlike other neural networks, this reduces the complexity of parameters.

 

To determine the gradients, recurrent neural networks use the backpropagation through time (BPTT) algorithm, which is slightly different from traditional backpropagation because it is specific to sequence data. BPTT works on the same principles as traditional backpropagation, in which the model trains itself by calculating errors from its output layer to its input layer. These calculations allow us to appropriately adjust and fit the model's parameters. BPTT differs from the traditional approach in that it sums errors at each time step, whereas feedforward networks do not have to sum errors because parameters are not shared across layers.

 

RNNs frequently encounter two issues during this process: exploding gradients and vanishing gradients. The size of the gradient, which is the slope of the loss function along the error curve, defines these issues. When the gradient is too small, it continues to shrink, updating the weight parameters until they become insignificant—that is, 0—and then stops. When this happens, the algorithm stops learning. When the gradient is too large, it explodes, resulting in an unstable model. In this case, the model weights will become too large and will be represented as NaN. One approach to addressing these issues is to reduce the number of hidden layers within the neural network, thereby removing some of the complexity in the RNN model.

 

Also Read | Learning Recurrent Neural Network

 

How do Recurrent Neural Networks Work? 

 

To fully comprehend RNNs, you must first understand "normal" feed-forward neural networks and sequential data.

 

Sequential data is simply ordered data in which related items follow one another. Financial data or the DNA sequence are two examples. The most common type of sequential data is probably time series data, which is simply a series of data points listed in chronological order.

 

The names RNNs and feed-forward neural networks come from the way they channel information.

 

In a feed-forward neural network, information only flows in one direction: from the input layer to the output layer via the hidden layers. The data flows directly through the network.

 

Feed-forward neural networks have no memory of the input they receive and are poor predictors. A feed-forward network has no concept of time order because it only considers the current input. It simply cannot recall anything from the past other than its training.

The information in an RNN is cycled through a loop. When it makes a decision, it considers the current input as well as what it has learned from previous inputs.

 

The two images below show the information flow differences between an RNN and a feed-forward neural network.

 

A traditional RNN has a short-term memory. They have a long-term memory when combined with an LSTM (more on that later).

 

Another effective way to explain the concept of recurrent neural network memory is to use an example: Consider a normal feed-forward neural network that receives the word "neuron" as input and processes it character by character. By the time it gets to the character "r," it has forgotten about "n," "e," and "u," making it nearly impossible for this type of neural network to predict which character will come next.

 

A recurrent neural network, on the other hand, can remember those characters due to its internal memory. It generates output, copies it, and loops it back into the network.

 

Also Read | 8 Types of Neural Networks

 

 

Types of RNN:

 

Traditional neural networks are inefficient when dealing with sequential data because they have independent input and output layers. As a result, a new neural network known as the Recurrent Neural Network was developed to store the results of previous outputs in internal memory. These outcomes are then used as inputs by the network. It can thus be used in applications such as pattern recognition, speech and voice recognition, natural language processing, and time series prediction.

 

Hidden layers in RNN serve as memory locations for storing the outputs of a layer in a loop.

 

The four most common types of Recurrent Neural Networks are as follows:

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Types of RNN

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1. Individualized instruction:

 

One-to-One RNNs are the most basic, with a single input and a single output. It functions as a traditional neural network with fixed input and output sizes. Image Classification contains the One-to-One application.

 

 

2. Many-to-One:

 

When given a single input, a one-to-many RNN produces multiple outputs. It accepts a fixed input size and returns a series of data outputs. It has applications in music generation and image captioning.

 

3. One-to-Many:

 

When a single output is required from multiple input units or a sequence of them, many-to-one is used. A fixed output requires a sequence of inputs. Sentiment Analysis is an example of a Recurrent Neural Network of this type.

 

 

4. Many-to-many:

 

Many-to-Many is a method for generating a series of output data from a series of input units.

 

This type of RNN is further subdivided into two subcategories:

 

• Equal Unit Size: The number of input and output units is the same in this case. Name-Entity Recognition is a popular application.
• Unequal Unit Size:  the inputs and outputs have different numbers of units. Its use can be found in Machine Translation.

 

 

Applications of RNN Networks:

 

Now that you know what a recurrent neural network is, let's look at a common application of RNNs.

 

1. Machine Translation:

 

RNN can be used to build a deep learning model that can translate text from one language to another without the need for human intervention. You can, for example, translate a text from your native language to English.

 

2. Text Creation:

 

RNNs can also be used to build a deep learning model for text generation. Based on the previous sequence of words/characters used in the text, a trained model learns the likelihood of occurrence of a word/character. A model can be trained at the character, n-gram, sentence, or paragraph level.

 

3. Captioning of images:

 

The process of creating text that describes the content of an image is known as image captioning. The image's content can depict the object as well as the action of the object on the image. In the image below, for example, the trained deep learning model using RNN can describe the image as "A lady in a green coat is reading a book under a tree.”

 

4. Recognition of Speech:

 

This is also known as Automatic Speech Recognition (ASR), and it is capable of converting human speech into written or text format. Don't mix up speech recognition and voice recognition; speech recognition primarily focuses on converting voice data into text, whereas voice recognition identifies the user's voice.

 

Speech recognition technologies that are used on a daily basis by various users include Alexa, Cortana, Google Assistant, and Siri.

 

5. Forecasting of Time Series:

 

After being trained on historical time-stamped data, an RNN can be used to create a time series prediction model that predicts the future outcome. The stock market is a good example.

 

You can use stock market data to build a machine learning model that can forecast future stock prices based on what the model learns from historical data. This can assist investors in making data-driven investment decisions.

 

Also Read | 8 Applications of Neural Networks

 

Summing Up:

 

An RNN has an internal memory that allows it to remember or memorize information from the input it receives, which aids the system in gaining context. As a result, if you have sequential data, such as a time series, an RNN will be a good fit to process it.

 

A CNN or Feed-Forward Neural Networks cannot do this because they cannot sort the correlation between the previous and next input.

 

RNN is used in popular products such as Google's voice search and Apple's Siri to process user input and predict the output.

 

Recurrent Neural Networks can be concluded to be a versatile tool that can be used in a variety of situations. They are used in a number of methods for language modeling and text generation. They are also used in speech recognition.

 

When combined with Convolutional Neural Networks, this type of neural network is used to generate labels for untagged images. This combination works incredibly well.

 

However, recurrent neural networks have one flaw. They struggle to learn long-term dependencies, which means they don't understand relationships between data that are separated by multiple steps.