As we will discuss later in the course, an important feature of machine learning $($ML$)$models is that they

employ non$-$linear transformations, often referred to as the $$activation function$,$which mimics the biology

of how our own neurons process information. One such example is tan$1($x$).$Having an intuition for both

this function and its derivative will be imperative towards grasping how ML models learn.

Consider y$=$tan$1($x$)$