Recurrent Neural Network (RNN)

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What Is a Recurrent Neural Network (RNN)?

A recurrent neural network (RNN) is a type of advanced artificial neural network (ANN) that converts sequential data input into a sequential output. An RNN can remember past inputs and data in sequence to help machine learning (ML) models generate more accurate predictions.

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RNN models are often used in artificial intelligence (AI) research solving natural language processing (NLP), speech recognition, machine translation (MT), image captioning, and sentiment analysis tasks.

Diagram explaining a recurrent neural network (RNN) with connected nodes, showing feedback loops where outputs of previous layers serve as inputs for subsequent layers.

Key Takeaways

  • A recurrent neural network is an f advanced artificial neural network where outputs from previous layers are fed as input to the next layer.
  • Recurrent neural networks can retain information from previous inputs.
  • RNNs are often used in natural language processing, speech recognition, image captioning, and sentiment analysis tasks.
  • An example of an RNN-powered service is Apple’s Siri assistant.
  • Notable issues with RNNs include the vanishing and the exploding gradient problem.

How Recurrent Neural Network Works

Now we’ve explained what an RNN is, let’s look at how they work. Recurrent neural networks feature a network of neurons connected by weights, and numerical values that determine the level of influence one neuron has on another.

In a traditional neural network, data will be fed forward, and processed in a single direction as it traverses through an input layer of neurons to a hidden layer, and finally through to the output layer.

But what is the basic concept of a recurrent neural network? Well, an RNN has a hidden state, or memory state, which takes information from previous inputs and feeds it into the next layer. This gives the model the ability to retain data from previous inputs.

This is important to note because the layers of standard neural networks don’t retain information from previous inputs.

As a result, recurrent neural networks have become one of the key solutions enabling generative AI (genAI) solutions

RNN Types

Diagram showing different types of recurrent neural networks (RNNs) including Standard RNNs, Long Short-Term Memory (LSTM), Bidirectional RNNs (BRRNs), Encoder-Decoder RNNs, and Gated Recurrent Units (GRUs).

There are a number of different types of recurrent neural networks in use today.

Some of these are as follows:

Standard RNNsBidirectional recurrent neural networks (BRRNs)Long short-term memory (LSTM)Gated recurrent units (GNUs)Encoder-decoder RNN

RNNS are a neural network with a hidden state that remembers information from previous inputs.

An RNN with two recurrent hidden layers that process data in two directions. One layer processes input sequences forward, and the other processes sequences backward.

An upgraded RNN that adds a memory cell that can be used to retain data long-term.

A type of RNN where a gated recurrent unit updates the hidden state of the neural network at each time step.

RNNs that include an encoder, hidden vector, and decoder. The encoder converts input sequences into a hidden vector; the decoder converts the hidden vector into the output sequence.

Recurrent Neural Network Components

Recurrent neural networks have a number of core components:

Input layer
Receives the initial input of sequence data and passes it to the hidden layer.
Hidden layer
Processes data taken from the input and collects information from the previously hidden layer, including past inputs, giving it the ability to process data in context.
Recurrent connection
Resides within the hidden layer and passes hidden state data to the next network layer.
Activation function
Combines and transforms input from the current input layer and the previous hidden layer before passing it to the output layer.
Output layer
Generates a decision or prediction based on information processed in previous steps.

Recurrent Neural Networks vs. Other Deep Learning Networks

Features Recurrent neural network Deep neural network Convolutional neural network
Definition Recurrent neural networks are neural networks with a hidden state that can retain information from previous inputs. A deep neural network is a neural network with three or more layers. A convolutional neural network can process image inputs and detect different features in an image.
Notable characteristics Can remember past inputs and process time series data. Contain multiple hidden layers and offer deeper processing capabilities than standard neural networks. Can identify features in images and videos.
Use cases Text generation, machine translation, voice recognition, image captioning, and time-series prediction. Natural language processing, image recognition, fraud detection, forecasting, chatbots. Image and video classification.

RNN Architecture

There are multiple different types of RNN architecture that are commonly used.

These are broken down below as follows:

One-to-one
A type of RNN with a single input and output. These types of networks are used to perform tasks such as image classification.
One-to-many
A type of RNN with a single input and multiple outputs. These types of networks are used to perform tasks like image captioning or music generation.
Many-to-one
A type of RNN with multiple inputs and a single output. Can be used to enable tasks like sentiment analysis or text classification.
Many-to-many architectures
A type of RNN with multiple inputs to several outputs. Often used to support machine translation tasks.

Recurrent Neural Network Examples

One of the most common examples of recurrent neural network-powered tools is machine translation services. Services like Google Translate or even voice assistants like Siri can use recurrent neural networks to recognize speech input and respond with a contextually relevant translation.

This means that if you want to use a tool like Google Translate to translate a phrase from English to Spanish, an RNN will offer a translation that matches your search intent and preserves the nuance of your initial input.

Recurrent Neural Network Use Cases

Some of the top use cases for recurrent neural networks include:

  • Text generationProcessing text and voice inputs and generating a relevant response to user questions and queries.
  • Machine translation: Translating text and voice inputs into multiple languages.
  • Voice recognition: Processing voice inputs and using them to identify an individual’s voice likeness.
  • Image captioning: Automatically adding captions to input images to help users identify what they depict.
  • Time-series prediction: Create a time series prediction model to predict future outcomes and events.

RNN Pros & Cons

There are a number of pros and cons to using RNNs:

Pros

  • Adepts at sequential tasks
  • Remembers context
  • Processes sequences of different lengths
  • Ensures high accuracy

Cons

  • Encounter issues with vanishing gradients
  • Struggle with exploding gradients
  • Have limited memory capacity
  • Show a tendency for recency bias

The Bottom Line

Now that we’ve looked at the definition of a recurrent neural network and how they work, it’s worth highlighting that these networks can come in many different shapes and sizes, and fulfill many different use cases.

RNNs can enable everything from AI-driven text generation to machine translation, voice recognition, image captioning, and time-series predictions.

FAQs

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Tim Keary
Technology Writer
Tim Keary
Technology Writer

Tim Keary is a technology writer and reporter covering AI, cybersecurity, and enterprise technology. Before joining Techopedia full-time in 2023, his work appeared on VentureBeat, Forbes Advisor, and other notable technology platforms, where he covered the latest trends and innovations in technology. He holds a Master’s degree in History from the University of Kent, where he learned of the value of breaking complex topics down into simple concepts. Outside of writing and conducting interviews, Tim produces music and trains in Mixed Martial Arts (MMA).