Machine Learning, Cost Function, Convex Optimization.
difference between our predicted result which we refer to as “Y^”(sometimes simply the vector “a”) and the actual vector “Y”(Keep in mind that here I am only talking about supervised learning ) So: where : Loss = ⟦Y — Y^⟧ (difference between predicted and actual output) So our goal is to minimize