Deep Learning - Deep Neural Network for Beginners Using Python - Combining Probabilities

The video tutorial explains how two models classify a point with different probabilities and how these probabilities can be combined. It highlights the error of exceeding probability limits and introduces the sigmoid function to correct this, ensuring the combined probability remains valid. The tutorial concludes with a discussion on combining linear boundaries and probabilities.

What does Model 1 classify the yellow point as, and what is its probability?

How do the probabilities from Model 1 and Model 2 combine, and what issue arises from this combination?

What is the first rule of probability mentioned in the text?

How can we convert a probability value greater than 1 into a valid probability?

What is the final probability of the yellow point belonging to the green family after applying the sigmoid function?