##### Deep neural netowrks # 1
In last tutorial series we wrote 2 layers neural networks model, now it's time to build deep neural network, where we could have whatever count of layers we want.
##### Deep neural netowrks # 2
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.
##### Deep neural netowrks # 3
Just like with forward propagation, we'll implement helper functions for backpropagation and calculate the gradient of the loss function with respect to the parameters.
##### Deep neural netowrks # 4
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.
##### Deep neural netowrks # 5
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.
##### Deep neural netowrks # 6
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.