Can we transfer the parameters of the trained RNN model for building a new RNN model?

Okay, so RNN models, of course, are used for sequential data. But this question should be looked at, maybe promoted broadly, of transfer learning, where you have trained one model, maybe on one task or one data set. And now you'd like to reuse some of that knowledge that you have trained in those models in a different setting, the setting could be a different task altogether, or maybe the same task in a different language, for example, so different variations are there. So transfer learning is the number one practical thing that works in the sense that there are not enough data sets available in all settings. So what people do is maybe there is a large public data set already available, they will train the model on that, and then they will fine tune or specialize the model for the data set that they have. And the data set that they have within the company or within their business unit will be much smaller. And the only way to train a large model with a small data is to benefit from transfer learning. So transfer learning is a very, very important skill. So you should consider, for example, one simple thing that you can consider doing is, I take a large model, like VGG. net, for example, which is a convolution neural network, and train it on something like image net, which is a large data set, and then fine tune it for some other task of image classification. This is a good way to learn how transfer learning works. And also to see the benefits that it provides. I think it is a very important practical skill to have.