On this post I’m going to be talking about the differences in classification algorithms / models for supervised tasks that involve a set of labels and inputs. The following formula is quite interesting: $$ \mathbf{x}(t) = \sum_{i=0}^{N} (\hat{y} - y)^2 $$ Do you like it?

The following is the video I’ve been talking about $n$. $$ \varphi = 1+\frac{1} {1+\frac{1} {1+\frac{1} {1+\cdots} } } $$