It's worth noting that apparently (as I learned lately) RNNs are going slightly out of fashion because they are hard to parallelize and have trouble remembering

It's worth noting that apparently (as I learned lately) RNNs are going slightly out of fashion because they are hard to parallelize and have trouble remembering important stuff at larger distances. Transformers are proposed as a possible solution - very roughly speaking, they use attention mechanisms instead of recurrent memory and can run in parallel. I have to say that while I understand the problems with recurrent nets (which I've used many times), I haven't yet grokked the alternatives. Here are some decently looking search results for you as starting points. Warning, these can be longer and heavier reads probably not for beginners. https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0 https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c7... (there's some sensationalism here to be fair) https://mchromiak.github.io/articles/2017/Sep/12/Transformer-Attention-is-all-you-need/ https://mchromiak.github.io/articles/2017/Sep/12/Transformer... https://www.analyticsvidhya.com/blog/2019/06/understanding-transformers-nlp-state-of-the-art-models/ https://www.analyticsvidhya.com/blog/2019/06/understanding-t... https://www.tensorflow.org/beta/tutorials/text/transformer https://www.tensorflow.org/beta/tutorials/text/transformer That being said, I think that understanding RNNs is very beneficial conceptually and nowadays there are relatively easy to use implementations that should be pretty good for many use cases.