The author probably shouldn&#x27;t have listed those as requirements - it&#x27;s not really needed to understand the article.<p>Classification (in context of the tutorial) is determining whether the data corresponds to a &#x27;male&#x27; or &#x27;female&#x27; (where &#x27;male&#x27; and &#x27;female&#x27; are the &#x27;class labels&#x27;)<p>The purpose of &#x27;Regularization&#x27; is to coax the network away from simply fitting an exact match to the training data (this wouldn&#x27;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 &#x27;penalty function&#x27;, so the network penalizes larger weights.<p>The pictures do a pretty good job of explaining everything else.