Explain how the recurrent neural network (RNN) processes data sequences

Explain how the recurrent neural network (RNN) processes data sequences

Answer:-

Recurrent Neural Network (RNN) Data sequences :-

1. Sequential Data Processing:
   • RNNs handle sequential data, processing it one time step at a time.
2. Hidden State Memory:
   • At each time step, the hidden state captures information from previous time steps, enabling the network to “remember” past information.
3. Recurrence Relation:
   • The hidden state is updated based on the current input and the previous hidden state, typically using a weighted sum followed by an activation function.
4. Dependency on Past States:
   • The output at each time step depends on both the current input and the previous hidden state.
5. Use Cases:
   • Useful for time-series prediction, natural language processing, speech recognition, and other tasks involving sequential data.

This graph visually demonstrates the recurrent structure and flow of data within an RNN over time, showing how the hidden state at each step depends on the previous hidden state and current input.

• Nodes and Variables:
  • y, L, o, h, x represent various variables in the network, where:
    • y refers to the Target,
    • L refers to the loss function,
    • o is the output at a time step,
    • h is the hidden state,
    • x is the input at each time step.
• Recurrent Connections:
  • The image highlights the unfolding of the recurrent connections over time, where the network is processed step by step, each step depending on the previous one.
• Weight Matrices:
  • W, U, V represent the weight matrices for:
    • W: Hidden-to-hidden connections,
    • U: Input-to-hidden connections,
    • V: Hidden-to-output connections.
• Unfolding Mechanism:
  • The “unfold” label refers to how the recurrent network, when computed over multiple time steps, is expanded into a sequence of operations, where each node corresponds to one time instance.