Backpropagation in RNN Explained

At the heart of backpropagation are operations and functions which can be elegantly represented as a computational graph. Let's see an example: consider the function f z(x y); It's computational graph representation is shown below: A computational graph is essentially a directed graph with functions and operations as nodes. Computing the outputs from the inputs is called the forward pass, and it's customary to show the forward pass above the edges of the graph. In the backward pass, we compute the gradients of the output wrt the inputs and show them below the edges. Here, we start from the end and go to the beginning computing gradients along the way.