# Backpropagation: the simplest form

doing backpropagation using pen and paper

We begin with a simple problem, we have two guys, Joris and Carl, who both want to be tall.

Joris is dutch and so he drinks a pint of milk a day, so that he may grow tall.

Carl is Swedish and so he eat meatballs with jam to grow tall.

We can represent this in a matrix:

X = [[1,0],[0,1]]

Where the first line is what Joris consumes, the second line is what Carl consumes, and the first column is pints of milk per day and the second column is plates of meatballs with jam per day.

It turns out that Joris’ plan worked and Carl’s didn’t, so out output matrix  y  is  [[1],[0]] , where Joris gets a 1 for being tall and Carl gets a 0 for being short.

Let’s now use a neural network to learn this information about the effects of milk and swedish meatball with jam on the growing tall.

We “randomly”, initiate our weights which gives us  w_1 = [[0],[1]] , as we know this is precisely wrong, since this will predict that drinking a pint of milk a day doesn’t make one tall, but eating swedish meatballs with jam will. Using  X * w_1 = hat y_1 , we get

 hat y_1 = [[0],[1]]

We can now compute by how much the network’s prediction missed the true values using  hat y_1 - y , which gives us our

 [[0],[1]] - [[1],[0]] = [[-1],[1]] = delta_1

We now use  delta_1  to update our weights:  w_1 - delta_1 = w_2:

 [[0],[1]] - [[-1],[1]] = [[1],[0]] = w_2

This is the backpropagation.

We can now make a new prediction using  X * w_2 = hat y_2 , which gives

 [[1],[0]]

Which is equal to the true values.

The network has now learned through backpropagation that drinking milk helps one grow tall, but eating swedish meatballs with jam does not.

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