Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are capable of learning from sequence data. This tutorial will provide an overview of RNNs, their architecture, and how they work.

What is RNN?

RNNs are designed to work with sequences of data, such as time series, text, or audio. Unlike traditional feedforward neural networks, RNNs have loops in their architecture, allowing them to maintain a "memory" of previous inputs.

Architecture

The basic architecture of an RNN consists of the following components:

  • Input Layer: This layer receives the input sequence.
  • Hidden Layer: This layer contains the weights and biases that are learned during the training process.
  • Output Layer: This layer produces the output sequence.

How RNN Works

RNNs process input sequences by iterating through each element and updating the hidden state. The updated hidden state is then used to generate the output for the current element.

Here's a simplified example of how an RNN works:

  1. The input sequence is fed into the input layer.
  2. The hidden state is initialized.
  3. The input and hidden state are combined to produce an output.
  4. The output is used to update the hidden state.
  5. Steps 3-4 are repeated for each element in the input sequence.

Challenges of RNN

While RNNs are powerful for sequence data, they have some limitations:

  • Vanishing Gradient Problem: RNNs can struggle to learn long-range dependencies due to the vanishing gradient problem.
  • Memory Overload: RNNs can become overwhelmed with too much information, leading to decreased performance.

Resources

For further reading, you can explore the following resources:

RNN_architecture

RNNs have a unique architecture that allows them to process sequences of data. The image above illustrates the basic components of an RNN.