This lecture explores the use of sigmoidal units as output functions in deep learning. The instructor explains the statistical interpretation of the output unit, deriving the optimal sigmoidal function and its application in single-class scenarios. The lecture also covers the logistic function, its rule of thumb, and the interpretation of the output as a probability. Key concepts include probability constraints, smoothness, and log-probability ratio.