#012 B Building a Deep Neural Network from scratch in Python Master Data Science

In this post we will see how to implement a deep Neural Network in Python from scratch. It isn't something that we will do often in practise, but it is good way to understand the inner workings of a Deep Learning. First we will import libraries we will use in the following code. In the following code we will define activation functions: \(sigmoid \), \(ReLU\) and \(tanh\) we will also save values that we will need for the backward propagation step and that are \(Z \) values, and after that we will define function which will output \(\textbf{dZ}\). So, to be clear, when we calculate activation of any hidden unit or of a hidden layer and also caches the value of \( Z {[l]} \) and we have set of functions called "backward" which outputs \( \textbf{dZ} \) values.