The sum of the product of the features and weights is given to the sigmoid or activation function. This is called the hypothesis. We begin with theories on what the output will look like, and then see how wrong we are when the results turn out to be different to what we actually require.
To realize how inaccurate our theories are, we require a loss, or cost, function:
The loss or cost function is the difference between the hypothesis and the real value that we know from the data. We need to add the sum function to make sure that the model accounts for all the examples and not only 1. The reason we square ...
