AI Terms & Concepts | AI Knowledge
Recurrent Neural Networks (RNNs)
What is a Recurrent Neural Network (in brief)?
A Recurrent Neural Network (RNN) is a deep learning model engineered to recognize patterns in sequences of data where order matters. Unlike standard feedforward neural networks which treat each input as independent, RNNs feature feedback loops that allow information to persist in an internal memory state—or hidden state—that carries information forward from previous steps in the sequence. This looping structure allows the network to maintain context over time, making it particularly effective for natural language processing, speech recognition, machine translation, and time-series forecasting. While basic RNNs can suffer from training challenges like exploding or vanishing gradients when dealing with long sequences, advanced variants like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) mitigate these issues to maintain longer-term dependencies.
Consider an Analogy:
Driving by Directions and Landmarks Without a Map.
What if you are trying to drive to a friend's house in a big, busy city that you don't know well and your GPS is not working, you only have a list of directions written down, like:
1. Go straight for two blocks.
2. Turn left at the large red building.
3. Go past the park on your right.
4. Turn right after you see the bridge.
5. Look for the house number 123.
As you don't have a live GPS map; you have to follow these instructions one by one, in order.
As you drive, you process the directions in their sequence. When you read "Turn left at the large red building," you process that instruction (the current data point).
Crucially, to follow the next instruction ("Go past the park"), you need to remember that you already turned left at the red building. Your understanding of "past the park" depends on the landmarks and turns you just processed. Your brain holds onto the recent sequence of events and instructions in its memory.
Even in congested traffic, you are constantly checking your surroundings against the landmarks you were told to look for, relying on your memory of the directions received so far to know if you're on the right track and when to perform the next action (turn, keep straight, etc.).
This process of going through a sequence of directions step by step, and needing to use your memory of the previous steps and landmarks to correctly interpret the current step and successfully navigate to your destination, is very much like how a Recurrent Neural Network works. It processes data points in a sequence, and it uses its internal "memory" of the data points processed earlier in the sequence to help it understand and act upon the current data point and the sequence as a whole.
Some Real-World Examples:
Predicting the Next Word on Your Phone Keyboard: Older smart keyboards rely on RNNs to analyse the words you have already typed in a sentence and predict the most likely next word. Because RNNs pass information from previous steps into the current step, the model maintains a short-term memory of your active phrase. This sequential context allows it to distinguish whether the word "bank" refers to a financial institution or a riverbank based on the words preceding it.
Simple Language Translation: Early neural machine translation models used RNNs to process a sentence word by word and generate its translation in another language. As the network reads each word, it updates an internal hidden state that acts as a running summary of the entire sentence so far. This allows the model to capture the overall context and produce a coherent translation rather than simply translating words isolated from one another.
Speech Recognition: AI systems that transcribe spoken language into text treat audio signals as a continuous sequence of sound features over time. RNNs process these audio frames step by step, using memory of previous acoustic patterns to clarify ambiguous sounds. This temporal context helps the system distinguish between similar-sounding words, such as "two," "to," and "too," based on the surrounding sentence structure.
And many more…