Understanding Recurrent Neural Networks: Applications and Examples

Recurrent neural networks are neural network models specialising in processing sequential data, such as text, speech, or time series information. Unlike standard neural networks, recurrent neural networks have a “memory” that allows them to develop a contextual understanding of the relationships within sequences. 

This introduction will cover recurrent neural networks, the different types, their applications, and their limitations. Read on to better grasp this essential artificial neural network architecture.

What is a Recurrent Neural Network? 

A recurrent neural network (RNN) is a deep learning model trained to process and convert a sequential data input into a specific sequential data output. Sequential data is data such as words, sentences, or time series, where sequential components interrelate based on complex semantics and syntax rules. 

An RNN is a software system comprising many interconnected components mimicking how humans perform sequential data conversions, such as translating text from one language to another. RNNs are mainly replaced by transformer-based artificial intelligence (AI) and large language models (LLM), which are much more efficient in sequential data processing.

How Does a Recurrent Neural Network Work? 

RNNs are made of neurons and data-processing nodes that work together to perform complex tasks. The neurons are organised as input, output, and hidden layers. The input layer receives the information to process, and the output layer provides the result. Data processing, analysis, and prediction take place in the hidden layer.  

Hidden layer

RNNs pass the sequential data they receive to the hidden layers one step at a time. However, they also have a self-looping or recurrent workflow: the hidden layer can remember and use previous inputs for future predictions in a short-term memory component. It uses the current input and the stored memory to predict the following sequence. 

For example, consider the sequence: Apple is red. You want the RNN to predict red when it receives the input sequence Apple is. When the hidden layer processes the word Apple, it stores a copy in its memory. 

Next, when it sees what the word is, it recalls Apple from its memory and understands the entire sequence: Apple is for context. It can then predict red for improved accuracy. RNNs are helpful in speech recognition, machine translation, and other language modelling tasks.

Training

Machine learning (ML) engineers train deep neural networks (RNNs) by feeding the model with training data and refining its performance. In ML, the neuron’s weights are signals that determine how influential the information learned during training is when predicting the output. Each layer in an RNN shares the same weight.   

ML engineers adjust weights to improve prediction accuracy. They use a backpropagation through time (BPTT) technique to calculate model error and adjust the weight accordingly. BPTT returns the output to the previous time step and recalculates the error rate. 

This method can identify which hidden state in the sequence is causing a significant error and readjust the weight to reduce the error margin.

What are the Types of Recurrent Neural Networks? 

RNNs are often characterised by one-to-one architecture: one input sequence is associated with one output. However, you can flexibly adjust them into various configurations for specific purposes. The following are several common RNN types.  

• One-to-many: This RNN type channels one input to several outputs. It enables linguistic applications like image captioning by generating a sentence from a single keyword.  
• Many-to-many: The model uses multiple inputs to predict multiple outputs. For example, you can create a language translator with an RNN, which analyses a sentence and correctly structures the words in a different language.  
• Many-to-one: Several inputs are mapped to an output. This is helpful in applications like sentiment analysis, where the model predicts customers’ sentiments, such as positive, negative, and neutral, from input testimonials.

Limitations of Recurrent Neural Networks   

Since the introduction of RNNs, ML engineers have made significant progress in natural language processing (NLP) applications with RNNs and their variants. However, the RNN model family has several limitations.  

• Exploding gradient: An RNN can wrongly predict the output in the initial training. It would help if you had several iterations to adjust the model’s parameters to reduce the error rate. 

Exploding gradient happens when the gradient increases exponentially until the RNN becomes unstable. When gradients become infinitely large, the RNN behaves erratically, resulting in performance issues such as overfitting.   

• Vanishing gradient: The vanishing gradient problem occurs when the model’s gradient approaches zero in training. When the gradient vanishes, the RNN fails to learn effectively from the training data, resulting in underfitting.  
• Slow training time: An RNN processes data sequentially, limiting its ability to process many texts efficiently. 

For example, an RNN model can analyse a buyer’s sentiment from a few sentences. However, it requires massive computing power, memory space, and time to summarise a page of an essay.

Conclusion

In summary, recurrent neural networks are great at understanding sequences, like words in a sentence. They’ve been used for language translation and speech recognition but have some issues. Other types of AI are replacing them in many cases, but they’ve paved the way for more advanced sequential data processing.