A Brief Summary of Maths Behind RNN (Recurrent Neural Networks)

In a feedforward neural network, we have X(input) and H(Hidden) and y(output). We can have as many hidden layers as we want but weights (W)for every hidden layer are and the weights for every neuron corresponding to the input are different. Above we have weights Wh0 and Wh1, which corresponds to two different layers, while Wh00, Wh01 and so on, represent different weights corresponding to different neuron and with respect to the input. The RNN cell contains a set of feed forward neural networks cause we have time steps. The RNN has sequential input, sequential output, multiple time-steps, and multiple hidden layers. Unlike FFNN, here we calculate hidden layer values not only from input values but also previous time step values and Weights ( W) at hidden layers are the same for time steps. Here is the complete picture for RNN and its Math.