The strategy that we'll adopt is as follows: our neural network will have one hidden layer (with neurons) connecting the input layer to the output layer. Note that we have more neurons in the hidden layer than in the input layer, as we want to enable the input layer to be represented in more dimensions:
Calculating the hidden layer unit values
We now assign weights to all of the connections. Note that these weights are selected randomly (based on Gaussian distribution) since it is the first time we're forward-propagating. In this specific case, let's start with initial weights that are between 0 and 1, but note that the final ...
