In the last tutorial series, we wrote 2 layers neural networks model, now it's time to build a deep neural network, where we could have whatever count of layers we want

Now when we have initialized our parameters, we will do the forward propagation module by implementing functions that we'll use when implementing the model.

Just like with the forward propagation, we'll implement helper functions for backpropagation and calculate the gradient of the loss function with respect to the parameters

In this part, we will implement the backward function for the whole network and we will also update the parameters of the model, using gradient descent

In this tutorial, we will use the functions we had implemented in the previous parts to build a deep network and apply it to cat vs dog classification

Training neural network requires specifying an initial value of the weights. You'll see that a well-chosen initialization method could improve learning and accuracy