Backpropagation Intuition
 Make a better intuition about backpropagation
 Technically a little more complicated

As from example, there's two unit + 1 unit biased unit from forward propagation  So here's in the example we first take x(1) take it to the neural networks down to the output.
 z(3)1 is the sigmoid function in layer 3 by take summation of (matrix_weight(2)10 x 1(bias_unit) + matrix_weight(2)11 x activation_unit(2)1).
 eventually a(4)1 is the prediction
 Backpropagation is doing the reverse of the forward. The process is really similar

The formula of cost function  if multiclass, then the formula will be added with summation of K unit classification.
 Because we are doing example of 1 output unit, and 1 example, we are also ignoring the regularization term
 For the purpose of the intuition, log will be ignored. We just want to know how close our network in predicting the output
 delta can be thought as an error for every activation value
 delta is actually the partial derivative of z, that if we change z, change the cost function, and eventually changing the actual cost
 The first step is intuitive, first final error = the final actual ouput  the final predicted output
 Then keep going backwards from last layer to first hidden layer
 By going reverse (from right to left) we are acquiring the delta (error value) by calculating [the previous error_value * matrix_weight]
 Layer indexed from 1, the input layer
 Why? We don't know the value of d(4)1
 Next, give a little better intuition about backpropagation
 Very effective algorithm eventhough a little harder to visualize