Welcome to the tutorial on Recurrent Neural Networks (RNNs). RNNs are a class of artificial neural networks that are well-suited for sequence prediction problems. In this guide, we'll explore the basics of RNNs, their architecture, and practical applications.
Overview
What is an RNN? RNNs are neural networks 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.
Why use RNNs? RNNs are particularly useful for tasks that involve sequential data, such as language translation, speech recognition, and stock price prediction.
Basic Structure
Input Layer The input layer receives the sequence of data points.
Hidden Layer(s) One or more hidden layers process the input data. RNNs use a loop to pass information from one layer to the next.
Output Layer The output layer produces the final prediction or classification.
Example: Language Translation
One of the most popular applications of RNNs is in language translation. Here's a simplified example of how an RNN can be used for this task:
- Input Sequence: "Hello"
- Hidden State: Processed through the hidden layers.
- Output Sequence: "Bonjour"
Challenges
- Vanishing Gradient Problem: RNNs can struggle with learning long-range dependencies due to the vanishing gradient problem.
- Computational Complexity: Training RNNs can be computationally expensive.
Resources
For further reading, check out our comprehensive guide on Deep Learning.