RNNs are pretty cool, right? But we have seen a problem in training the RNNs called the vanishing gradient problem. Let's explore that a bit. The sky is __. An RNN can easily predict the last word as blue based on the information it has seen. But an RNN cannot cover long-term dependencies. What does that mean? Let's say Archie lived in China for 20 years. He loves listening to good music. He is a very big comic fan. He is fluent in _. Now, you would predict the blank as Chinese. How did you predict that? Because you understood that Archie lived for 20 years in China, you thought he might be fluent in Chinese. But an RNN cannot retain all of this information in memory to say that Archie is fluent in Chinese. Due to the vanishing gradient problem, it cannot recollect/remember the information for a long time in memory. How do we solve that?

Here...