What is RNN and how does it work?

August 23, 2025

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A Recurrent Neural Network (RNN) is a type of artificial neural network designed to handle sequential data, such as text, speech, time-series data, or video frames. Unlike traditional feedforward networks, which treat inputs as independent, RNNs use loops (recurrence) to remember information from previous steps in the sequence.

🔹 How RNN Works

In an RNN, the output at each time step depends not only on the current input but also on the hidden state, which carries information from previous inputs.
This hidden state acts as the network’s memory, allowing it to capture context across sequences.
Mathematically:
```
h_t = f(W_hh * h_(t-1) + W_xh * x_t + b_h)  
y_t = f(W_hy * h_t + b_y)
```
- x_t = input at time t
- h_t = hidden state at time t
- y_t = output at time t
- W = weight matrices, b = biases
- f = activation function (like tanh, ReLU, softmax)

🔹 Example

If an RNN reads the sentence “I love AI” word by word:

At “I”, it stores this info in hidden state.
At “love”, it combines current word + memory of “I”.
At “AI”, it uses memory of both previous words to understand context.

🔹 Limitations

Vanishing & exploding gradients: When sequences are long, RNNs struggle to remember earlier steps.
Short-term memory bias: Works well for short sequences, but not for long-term dependencies.

🔹 Solutions

LSTMs (Long Short-Term Memory) and GRUs (Gated Recurrent Units) were developed to overcome these issues. They use gates to control what to keep, update, or forget in memory.

🔹 Applications of RNN

Natural Language Processing (text prediction, translation, chatbots)
Speech recognition
Time-series forecasting (stock prices, weather)
Video analysis

👉 In short: RNNs are neural networks with memory—they process data step by step, remembering past inputs to make better predictions in sequential tasks.

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