Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are designed to recognize patterns in sequences of data, such as time series or natural language. Unlike feedforward neural networks, RNNs have loops allowing information to persist, making them capable of learning from sequential data.
Key Concepts
- Sequential Data: RNNs are especially suited for processing sequential data, which is data that comes in a sequence over time.
- Weights and Biases: RNNs use weights and biases to learn from data.
- Backpropagation Through Time (BPTT): This is a method used to train RNNs by propagating the error backwards through time.
How RNNs Work
RNNs work by maintaining a hidden state that captures information from the previous input. This allows the network to retain information over time.
Example
- Input:
[a, b, c] - Hidden State: Initially empty
- Output:
[h1, h2, h3]
Each h represents the hidden state at each step.
Limitations
RNNs have some limitations, such as vanishing and exploding gradients, which can make them difficult to train.
Resources
For more information on RNNs, check out our Introduction to Deep Learning.
Here's an example of how RNNs can be visualized:
The image above shows a simple RNN architecture.