Recurrent Neural Networks (RNN): Neural Architecture for Sequential Data

Recurrent Neural Networks (RNNs) are deep learning architectures designed to model sequential data by retaining information across time steps. Unlike feedforward networks, RNNs use recurrent connections and hidden states, enabling memory of previous inputs and making them suitable for time-dependent tasks.

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Core Characteristics of RNNs

Sequential Processing
RNNs handle ordered inputs such as text, speech, or temporal signals, where past context influences predictions.
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Hidden State Representation
Each step updates a hidden state based on current input and previous state:
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hₜ = f(Wₕₕhₜ₋₁ + Wₓₕxₜ + b)

allowing the model to store temporal patterns.
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Backpropagation Through Time (BPTT)
Training unfolds the network across all time steps. Gradients accumulate over the sequence, making RNNs prone to vanishing or exploding gradients.

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Variants of RNNs

LSTM (Long Short-Term Memory)
Introduces gates (input, forget, output) and memory cells to maintain information over long sequences, solving vanishing gradient problems.
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GRU (Gated Recurrent Unit)
Simplified LSTM with fewer parameters and a single update gate, offering faster training while preserving long-range memory.
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Bidirectional RNN
Processes sequences forward and backward, improving context understanding in applications like NLP.

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Applications of RNNs

Natural Language Processing
Used in text generation, machine translation, sentiment analysis, and speech-to-text models.
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Forecasting
Suitable for time-series prediction tasks including finance, weather forecasting, and anomaly detection.
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Speech and Audio Processing
Processes audio signals sequentially for speech recognition and synthesis.
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Sequential Media Processing
Applicable in music generation, video frame modeling, and action recognition.

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Related Terms

• LSTM
  
  
• Neural Architecture Search