recurrent neural networks common to iterate using lstms and grusand take the best you can read more about this research at discussing the details of grus is beyond the scope of this readers are encouraged to explore this topic further on their own ummary in this we covered the basics of recurrent neural networks (rnnsthe key takeaway points from this are the notion of the hidden statetraining rnns via unrolling (backpropagation through time)the problem of vanishing and exploding gradientsand long short-term memory (lstmnetworks it is important to internalize how rnns contain internal/hidden state that allow them to make predictions on sequence of inputs--an ability that goes beyond conventional neural networks