What are the differences between Transformers and RNNs?

What are the differences between Transformers and RNNs?

Transformers and RNNs are both neural network architectures used for sequence data, but they differ significantly. RNNs process data sequentially, which can lead to issues with long-range dependencies. In contrast, Transformers use self-attention mechanisms, allowing them to consider all parts of the input simultaneously, making them more efficient for longer sequences.

Can you explain how self-attention works in Transformers?

Sure! Self-attention allows the model to weigh the importance of different words in a sequence relative to each other. It computes a score for each word pair, which helps the model focus on relevant words when generating an output, regardless of their position in the sequence.

Why are Transformers preferred over RNNs in many applications?

Transformers are often preferred because they handle long-range dependencies better, are more parallelizable, and generally achieve higher performance on tasks like translation and text generation. Their architecture allows for faster training times and better scalability.

Are there any scenarios where RNNs are still useful?

Yes, RNNs can still be useful in scenarios with limited data or where the sequence length is short. They are also simpler and can be effective for tasks like time series prediction or when computational resources are constrained.