Simple explanation of recurrent neural networks (RNNs)

Recurrent neural networks (RNNs) are a special type of artificial neural network that are particularly well suited for processing sequential data. Unlike traditional neural networks, which treat all inputs independently, RNNs have a “memory” function that allows them to store and use information about previous inputs. This makes them particularly useful for tasks such as speech recognition, machine translation, and even predicting stock prices.

What are neural networks?

Before we dive deeper into RNNs, it is helpful to have a basic understanding of neural networks. A neural network is a computer model that mimics the human brain. It consists of many interconnected “neurons” arranged in layers. These neurons work together to recognize patterns in data and learn.

Neural networks typically consist of three types of layers:

1. Input layer: This layer receives the raw data.
   
   
2. Hidden layers: These process the data through a series of mathematical operations.
   
   
3. Output layer: This layer delivers the result of the processing.

What makes RNNs special?

What makes RNNs special is their ability to store information over time or across sequences. This means that they can take into account not only the current input, but also previous inputs. This is achieved through a feedback loop, in which the output of a neuron is fed back into the same neuron as input. In this way, the network can “remember” what has happened before.

How do RNNs work?

Imagine reading a sentence word by word. A conventional neural network would consider each word in isolation. An RNN, on the other hand, remembers the previous words when it reads the next word. This is similar to how humans understand a sentence, where each new word is interpreted in the context of the previous words.

In technical terms, an RNN uses internal memory to store information about previous calculations. Each time the RNN receives a new input, it combines this input with the stored state from the previous inputs. This allows it to analyze sequences and recognize complex patterns.

A simple example is an RNN that is supposed to predict the next number in a sequence. If the sequence is 1, 2, 3, the RNN can learn that the next number should be 4 by remembering the previous numbers.

Applications of RNNs

RNNs are used in many areas where sequences are important. Some examples are:

1. Speech recognition: RNNs can recognize spoken words and convert them into text by taking into account the context of the entire statement. This is useful for applications such as voice assistants (e.g., Siri or Google Assistant).
   
   
2. Machine translation: RNNs can translate entire sentences from one language to another by understanding the meaning of each word in the context of the entire sentence. This is often used in translation services such as Google Translate.
   
   
3. Text generation: RNNs can generate new text that is stylistically similar to a given text, such as writing poems or stories. Authors and content creators use this technology to create creative content or develop ideas.
   
   
4. Time series analysis: RNNs are used to predict future values in time series data, such as weather forecasting or financial market analysis.
   
   
5. Image caption generation: RNNs can be used to generate image descriptions by converting visual inputs into descriptive text. This is particularly useful for applications in image processing and computer vision.

Recurrent neural networks (RNNs) are a powerful technology for processing sequential data. With their wide range of applications, RNNs remain an important component of modern artificial intelligence and will continue to play a central role in the further development of AI technologies.