Explain RNN (Recurrent Neural Network).

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A Recurrent Neural Network (RNN) is a type of artificial neural network specifically designed to handle sequential data such as text, speech, time-series, and video frames. Unlike traditional feedforward neural networks, RNNs have a special architecture that allows them to retain memory of previous inputs through loops in the network. This memory makes them effective at modeling relationships where the current output depends not just on the current input, but also on prior inputs.

How RNN Works

In a standard RNN, input data is processed one step at a time. At each step, the network takes two things as input:

1. The current input (e.g., a word in a sentence).

2. The hidden state from the previous step (which acts as memory).

This hidden state is updated repeatedly as the sequence progresses, and the final state (or the entire sequence of states) can be used to make predictions.

Mathematically:

$$h_t = f(W_h \cdot h_{t-1} + W_x \cdot x_t + b)$$

where:

• $h_t$ = hidden state at time step t

• $x_t$ = input at time step t

• $W_h, W_x$ = weight matrices

• $f$ = activation function (like tanh or ReLU)

Applications of RNN

• Natural Language Processing (NLP): text generation, sentiment analysis, machine translation

• Speech Recognition: converting audio signals into text

• Time-Series Prediction: stock prices, weather forecasting

• Sequence Classification: activity recognition, DNA sequence analysis

Challenges with RNN

• Vanishing Gradient Problem: Gradients shrink during backpropagation, making it hard to learn long-term dependencies.

• Exploding Gradient Problem: Gradients grow too large, causing instability.

Solutions

To overcome these issues, advanced variants were developed:

• LSTM (Long Short-Term Memory): uses gates to retain long-term dependencies.

• GRU (Gated Recurrent Unit): simpler than LSTM, but still effective for sequence modeling.

👉 In short, RNNs are powerful for handling sequential data where order matters, but due to their limitations, LSTMs and GRUs are often preferred in real-world applications.

🔑Read More:

Explain CNN (Convolutional Neural Network).

What are activation functions, and why are they used?

What is gradient descent?

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