The author probably shouldn't have listed those as requirements - it's not really needed to understand the article. Classification (in context of the tutorial)

The author probably shouldn't have listed those as requirements - it's not really needed to understand the article. Classification (in context of the tutorial) is determining whether the data corresponds to a 'male' or 'female' (where 'male' and 'female' are the 'class labels') The purpose of 'Regularization' is to coax the network away from simply fitting an exact match to the training data (this wouldn't be useful because it would do poorly at any new data). In the context of the tutorial, they add the L1 norm to the 'penalty function', so the network penalizes larger weights. The pictures do a pretty good job of explaining everything else.