Difference between RNN and seq2seq

I saw in this first lecture, that seq2seq model is shown as RNN. So, can anybody say me clear difference between them?

Hello @someone555777!

The main difference between the RNN and seq2seq models lies in their purposes and architectures. RNNs are general sequential models that process data one element at a time, updating hidden states as they progress. On the other hand, seq2seq models are designed specifically for sequence-to-sequence tasks, like translation, and consist of both an encoder and a decoder RNN to handle the input and output sequences effectively.

You can say that seq2seq models are a specific type of RNN. Seq2seq models consist of two main components: an encoder RNN and a decoder RNN. So both the encoder and decoder are implemented using the RNN.

So, in essence, seq2seq models build upon the capabilities of RNNs to address specific tasks involving sequence-to-sequence transformations, such as machine translation, text summarization, and more.

I hope this helps you and feel free to ask for another clarification if needed.
Best Regards,