Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are well-suited for sequence prediction problems. They are designed to work with input data that is sequential in nature, such as time series data, text, and audio.
Key Concepts of RNNs
Here are some key concepts that you should be familiar with when learning about RNNs:
- Inputs and Outputs: RNNs take sequential data as input and produce sequential data as output.
- Hidden State: RNNs have a hidden state that keeps track of information from previous inputs.
- Backpropagation Through Time (BPTT): This is the process of training RNNs by propagating the error backwards through time.
RNN Applications
RNNs have a wide range of applications, including:
- Language Modeling
- Machine Translation
- Speech Recognition
- Stock Price Prediction
- Medical Sequence Analysis
Getting Started with RNNs
To get started with RNNs, we recommend checking out the following tutorials:
Example: Language Modeling
Language modeling is a task where an RNN predicts the next word in a sentence based on the previous words. Here's a simple example of how it works:
- Input: "The cat sat on the"
- Output: "mat"
By using an RNN for language modeling, you can generate coherent sentences and even write stories!
Here's an image of an RNN to help visualize its structure:
For more detailed information on RNNs, don't forget to check out our RNN Tutorials.