Recurrent Neural Networks (RNNs) are a class of artificial neural networks that are capable of learning from sequential data. They are particularly useful for tasks that involve time series analysis, natural language processing, and other sequential data processing tasks.
RNN Overview
What is an RNN? RNNs are designed to work with sequences of data. Unlike traditional neural networks that process data in batches, RNNs process data sequentially, which makes them suitable for tasks like language modeling and speech recognition.
Key Components
- Input Layer: The first layer of the RNN that receives the input sequence.
- Hidden Layer: The layer(s) that process the input sequence and maintain state information.
- Output Layer: The layer that produces the output based on the processed input sequence.
Basic RNN Structure
- Input: The input sequence is fed into the RNN.
- Hidden State: The RNN maintains a hidden state that captures information about the sequence processed so far.
- Weight Updates: During training, the weights of the RNN are updated to minimize the difference between the predicted output and the actual output.
Example Use Cases
- Language Modeling: Predicting the next word in a sentence.
- Speech Recognition: Converting spoken words into written text.
- Time Series Analysis: Forecasting stock prices or weather patterns.
RNN Structure
Learning Resources
For more in-depth learning about RNNs, you can visit our Deep Learning Tutorial.
If you're interested in exploring the mathematics behind RNNs, check out our Mathematics of Neural Networks tutorial.