What is a Recurrent Neural Network (RNN)?
What is a Recurrent Neural Network (RNN)?
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Updated 19 Apr 2026
A Recurrent Neural Network (RNN) is a type of neural network designed to process sequential data by remembering previous inputs using internal memory. It’s commonly used for tasks like text prediction, speech recognition, and time-series analysis.
A Recurrent Neural Network (RNN) is a type of neural network designed to work with sequential data—data where order matters, like text, time series, or speech.
1. Simple Intuition
Normal neural networks treat inputs independently.
RNNs do something different:
Example
Sentence:
An RNN can predict:
2. How RNN Works
At each step, an RNN takes:
Core Idea (Memory Flow)
Instead of starting fresh each time:
Mathematical View
3. Unrolled View (Important Concept)
Instead of a loop, imagine RNN like a chain:
Each step passes information forward.
4. Where RNNs Are Used
Natural Language Processing (NLP)
Time Series
Speech Recognition
5. Problem with Basic RNN
RNNs struggle with long-term memory.
Issue:
This is called:
6. Advanced Variants
To solve this, we use:
1. LSTM (Long Short-Term Memory)
2. GRU (Gated Recurrent Unit)
7. RNN vs Transformer
Modern models like GPT use Transformers instead of RNNs.
8. Real-Life Analogy
Think of reading a story:
9. One-Line Summary