Recurrent Neural Networks
An AI model architecture that processes sequences by remembering previous inputs to predict what comes next.
In Plain English
A Recurrent Neural Network (RNN) is an AI architecture designed to understand and predict patterns in data that unfold over time—like sentences, stock prices, or heartbeat readings. Unlike simpler models that treat each piece of information independently, an RNN maintains a kind of "memory" of what it has seen before. As it reads through a sequence one step at a time, it updates this internal memory and uses it to make better predictions about what comes next. This makes RNNs particularly useful for tasks where order and history matter, such as language translation or forecasting.
💡Real-World Example
Your phone's keyboard uses an RNN-like approach to predict the next word you'll type. When you write "I love to eat," the model has seen you type the first four words in order. It remembers that sequence and uses it to suggest "pizza" or "breakfast" as the next word, rather than suggesting words that wouldn't fit the flow of your sentence.
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