Why RNN?
Many types of data are sequential: time series (stock prices), text (sentences), audio (speech), video (frames). Traditional neural networks assume inputs are independent. RNNs (Recurrent Neural Networks) are designed to handle sequences by maintaining a hidden state that captures information from previous steps.
RNNs have loops that allow information to persist, making them suitable for sequential data.
Limitations of Feed‑Forward Networks for Sequences
- Fixed input size – cannot handle variable‑length sequences.
- No memory – each input is processed independently.
- Cannot capture temporal dependencies (e.g., the word after "I ate" depends on previous words).
How RNNs Solve This
RNNs process one element at a time and pass a hidden state to the next step. The hidden state acts as memory, carrying information forward.
h_t = activation(W_h * h_{t-1} + W_x * x_t + b)Examples of Sequential Data
- Text sentiment: "The movie was not good" – the word "not" changes meaning of "good".
- Stock price prediction: tomorrow's price depends on past prices.
- Speech recognition: sound at time t depends on previous sounds.
Two Minute Drill
- RNNs handle variable‑length sequences.
- Hidden state carries memory across time steps.
- Used for text, time series, audio, video.
- Standard networks have no memory.
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