Machine Learning, Deep Learning, Recurrent Neural Network, Text Generation, Neural Networks.
the previous time step’s hidden state: h(t)​ = f(U x(t)​ + W h(t−1)​). The function f is taken to be a non-linear transformation such as tanh, ReLU. Weights: The RNN has input to hidden connections parameterized by a weight matrix U, hidden-to-hidden recurrent connections parameterized by a weight