Recurrent neural network in Python: how does it work?

In the field of machine learning, specifically within Deep Learning, there are many types of architectures for neural networks. Each of them specializes in performing a specific task. Recurrent neural networks are often used to work with text and stream data. In this article we’ll go through what they are and how they can be created using Python.

Python is one of the most popular programming languages, especially for machine learning development. A field that has multiple opportunities for all those who decide to specialize through a master’s degree or a Python Programming course.

Throughout this text we will also see the training possibilities and how a good specialization in Python can propel you in one of the professional fields with the greatest projection today. Interested? Stay with us, here we go!

 

What are recurrent neural networks?

As we said, recurrent neural networks (RNN) are a type of neural network. Neural networks are an important part of Deep Learning, which, in turn, is a type of machine learning, one of the fields of study of Artificial Intelligence. This type of neural network is specialized in processing sequential data or time series.

What does this mean? Well, a RNN can help in the generation of predictive models from a sequence of historical data. An example of this is the development of a predictive model of a company’s sales volume. This, in turn, helps to better control stock and improve the company’s production processes.

RNNs are especially useful for the development of natural language processing.

The architecture of recurrent neural networks makes it easy for the program or application developed with them to remember and forget the information it processes. In this way, this type of machine learning model is capable of remembering processed data at the beginning of sequencing in order to associate it with new data that is being analyzed.

This also makes recurrent neural networks especially useful for text creation. In fact, they are able to analyze fragments of text and generate new content from them. Thus, one of its applications is finding this in the development of predictive text applications.

 

How does a recurrent neural network work?

A recurrent neural network, like any other type of neural network, is made up of different layers made up of nodes or neurons. In this case, the recurrent neuron presents a series of characteristics that will help us understand how this machine learning model works.

Generally speaking, the nodes used in other types of models have the capacity to transfer information to a single address. That is, they only send it forward. A recurrent neuron, unlike others, is capable of sending information forwards but it is also capable of sending it backwards.

At each instant, a recurring neuron is receiving information from the input data of the previous one, as well as from its output in order to be able to generate its own output from the processed data.

In this way, through recurrent neural networks, information can be recovered from the previous steps, making the program able to establish a relationship with what is happening at the current processing time. For practical purposes, this is not very efficient, so certain architectures must be generated that allow the application of tools that optimize this process.

Each of the recurrent neurons that make up the layers of a RNN has two sets of parameters, one that is applied to the data input of the previous layer and another that is applied to the output data. Through the backpropagation method, this process can be optimized to reduce errors and improve the processing of the neural network.

 

Types of recurrent neural networks

There are different types of recurrent neural networks depending on the input and output format that you want to obtain:

• One-to-many: an architecture for RNNs that allows the input of data and the output of a sequence of data. An example of this can be found in neural networks trained to describe an image. In this case, the network takes the image as input data, but returns a descriptive text as output data.
• Many-to-one: in this case, the RNN works from a set of input data to provide single data as output. Here, the opposite example is applied: the AI model receives a description of an image (for example, an image of a dog), and returns a generated image. An example of this is DALL-E, a web application that allows users to enter a text so that the AI generates an image.
• Many-to-many: In this case, as  you can guess, we are dealing with a RNN that starts from a more or less large data set to process various data in its output. In this case, an example of this are text generation applications, from a given text, they are capable of generating new content, as InferKit does, or more developed applications focused on creating content for blogs and social networks such as Dupla AI.

Thus, having seen the types of RNN that exist, it’s possible to infer some of their main applications:

• Smart text translation
• Smart chatbots
• Sales prediction
• Virtual assistants
• Image recognition

This is just a taste of the potential of recurrent neural networks. This type of machine learning model is combined with others such as generating antagonistic networks or convolutional ones for the development and progress in this field of Artificial Intelligence research.

 

Learn Python programming!

Now that you’ve mastered the concept of recurrent neural networks, it’s time to start creating your own machine learning algorithms with Python. In order to do this, you will have to train. At Toyio School we offer our Python Programming course which will help you learn the fundamentals of this language and allow you to work in this sector.

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