RNN (Recurrent Neural Network) is a type of neural architecture designed to handle sequential data, such as time series, text, or speech. Unlike traditional neural networks, RNNs have loops that allow information to persist through multiple steps. This makes them capable of capturing patterns and dependencies over time, enabling them to perform tasks like language modeling, machine translation, and speech recognition with greater context-awareness.
How It Works:
- Recurrent Connections: RNNs process input data step-by-step, passing hidden states from one time step to the next.
- Memory of Past Inputs: By maintaining an internal state, RNNs can use information from earlier inputs to influence future predictions.
- Variations and Improvements: Enhanced architectures like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) address issues like vanishing or exploding gradients, improving long-term dependency learning.
Why It Matters:
RNNs laid the groundwork for advanced sequence modeling, enabling more natural handling of language, speech, and time-dependent signals. Although more modern architectures like Transformers have become popular, RNNs remain foundational, offering insights into how neural networks can remember and utilize contextual information across sequences.
