In the previous subsections, we explain supervised and unsupervised learning. Regardless of which machine learning algorithm is used, our main challenge is regarding issues with optimization. In optimization functions, we are actually trying to minimize the loss function. Imagine a case where you are trying to optimize your monthly savings. In a closed state, what you will do is minimize your spending, in other words, minimize your loss function.
A very common way to build a loss function is starting with the difference between the predicted value and the actual value. In general, we try to estimate the parameters of our model, and then prediction is made. The main measurement that we can use to evaluate how good our prediction is involves calculating the difference between the actual values:
In different models, different loss functions are used. For...