Recurrent Neural Network Visualizer

What are Recurrent Neural Networks (RNNs)?

RNNs are neural networks designed to process sequential data by maintaining a "memory" (hidden state) that gets updated at each step. Unlike feedforward networks, RNNs have loops that allow information to persist across timesteps, making them ideal for tasks like language modeling, time series prediction, and speech recognition.

Key concepts:

• Hidden State: The network's memory that carries information from previous timesteps
• Recurrent Connection: Feedback loop that passes hidden state to the next timestep
• Sequence Processing: Input is processed one element at a time, left to right
• Temporal Dependencies: RNNs can learn patterns that span across time
• Vanishing Gradients: Common problem where gradients shrink exponentially over long sequences
• Unrolling: Visualizing the RNN as a chain of copies, one for each timestep