In this post, discover what a recurrent neural network is, the various types of RNNs, and the benefits of using this important AI tool.
Artificial intelligence (AI) is a broad term that encompasses many different aspects of technology. While many people assume that AI is a stand-alone tool, the truth is that artificial intelligence relies on many sets of data and algorithms to work properly.
Neural networks are one of the foundational components to AI software and applications, particularly for business use cases. Since AI is designed to mimic human thought processes, neural networks tie those connections together to generate results. In neural networks, connections fire in similar pathways as the human brain.
In this quick guide, we’ll focus on one specific type of neural network–the recurrent neural network. Keep reading to learn what it is, how it differs from other neural networks, and why it matters for life and business.
What is a recurrent neural network?
A recurrent neural network (RNN) is one class or subgroup of many possible neural networks. In an RNN, the network uses sequential data to model results and generate new AI-based predictions.
A recurrent neural network falls under the “deep learning” category of artificial intelligence. This means that RNNs are the perfect fit for applications such as:
Brief history of recurrent neural networks
Although applications are more widespread than ever before (with tools like voice recognition and speech-to-text technology), recurrent neural networks aren’t a new invention. In fact, they’ve likely been around since the 1980s.
Recurrent neural networks were first based on work by David Rumelhart, an acclaimed psychologist who contributed greatly to the field of artificial intelligence. Once the invention of Long Short-Term Memory (LTSM) took place in the 1990s, the use of speech recognition tools using RNNs began to accelerate.
Fundamentals of RNN memory
A recurrent neural network differs from a traditional “feed forward” network. Feed forward simply implies that the network receives an input and makes a decision based on that one input alone. While the feed forward network uses its basic algorithms and training, it doesn’t have memory capabilities.
In contrast, a recurrent neural network loops back to its input sources. The RNN makes new, predictive decisions not just based on a single input, but also on past experiences. This happens through the use of a hidden layer, which exists in between the input and output layers.
The hidden layer is the component of an RNN that stores information about a sequence and makes future recommendations accordingly.
The hidden layer makes it possible for recurrent neural networks to not only analyze new incoming data, but to compare that data to past data it experienced, as well.
What is a recurrent neural network used for?
Recurrent neural networks are usually paired with a deep learning application. Most often, this happens within a few common use cases.
Voice and speech recognition
Because recurrent neural networks can remember a greater number of past inputs than other algorithms, they’re particularly useful at making independent predictions. While the list above is a good start, there’s no limit to what a recurring neural network can help the user accomplish.
Whether we realize it or not, many of us encounter RNNs on a daily basis. For example, if you use a speech-to-text feature on your mobile phone, you may be leveraging the power of a highly advanced neural network without realizing it.
Types of recurrent neural networks
Unlike other models, RNNs are not constrained by one input/output model. Instead, their inputs and outputs might vary in length or change based on the given use case and application. There are four basic types of recurrent neural networks.
One-to-one: 1 input leads to 1 output
One-to-many: 1 input leads to multiple outputs
Many-to-one: Multiple inputs traverse the hidden layer to create 1 output
Many-to-many: Multiple inputs traverse the hidden layer to create several outputs
Training a neural network
In order for a recurrent neural network (or any kind of neural network) to function properly, it must receive programming and training. This is the part of the process in which the “artificial” aspect of AI begins to take off.
According to Michael Phi, Software and Machine Learning Research Engineer, training a recurrent neural network involves three important steps.
The RNN passes from input to output and makes an initial prediction
Then, the RNN must compare that prediction to a foundational truth; this is accomplished by using a loss function.
Finally, the error value (determined by the loss function) calculates the gradient for each node in the neural network.
This process relies on back propagation, which is a standard AI term for training networks to produce results based on past functioning.
What are the benefits of a recurrent neural network?
Recurrent neural networks can be advantageous for designers and developers who need a network that leverages past data. The benefits of RNNs include:
Access to usable “memory” that the network uses in future decisions
Ability to train the network using Long Short-Term Memory (a critical AI function)
Reliable results for any use case involving a time series
Flexibility to adapt to multiple deep learning use cases
How to differentiate RNN vs. other neural networks
Although recurrent neural networks are powerful, they aren’t the only kind of neural network available for widespread use and application.
Another popular choice is the convolutional neural network (CNN). In contrast to RNNs, CNNs are more suited to spatial data and images. While RNNs have a circular network for processing inputs and outputs, CNNs use filters and layers instead.
When comparing any neural network to another one, consider the following components.
Preferred or ideal use case
Possible adaptability for future applications
Neural networks are the backbone to reliable and usable deep learning platforms. As business tools become more advanced, it’s important to understand how these networks operate behind the scenes to power the tools we use and rely on every day.
For businesses to remain competitive, tech teams should be mindful of possible use cases that involve recurrent neural networks and other AI algorithms.