Logistic Regression - Intro, Loss Function, and Gradient

In this video we introduce logistic regression as a tool for binary classification. We talk through the choice of the logistic sigmoid function for modeling probabilities, and we proceed to define a loss function over data. Then we find the gradient of that loss function with respect to the weight vector and close with a few remarks about optimization using gradient descent. In a follow-up video, I implement Logistic Regression from scratch in Python using these formulas: https://youtu.be/2Vagk_ovHQs