This easiest type of RNN consists of a single hidden layer where weights are shared across time steps. Vanilla RNNs are appropriate for learning short-term dependencies however are restricted by the vanishing gradient problem Digital Logistics Solutions, which hampers long-sequence studying. These items have an inner hidden state that acts as reminiscence that retains data
