Reinforcement Learning and Deep RL Python Theory and Projects - DNN What Is Loss Function Exercise Solution

The video tutorial explains the concept of true and predicted labels in binary classification, where the true label is either 0 or 1, and the predicted label is a probability between 0 and 1. It introduces the loss function used in binary classification, specifically focusing on binary cross entropy loss. The tutorial details how the loss is calculated based on whether the true label is 1 or 0, emphasizing the role of logarithms in determining the loss value. The video concludes with a concise formula for binary cross entropy loss, highlighting its importance in binary classification tasks.

What is the range of the predicted label in binary classification?

• 0 to 100

• -1 to 1

• 0 to 1

• 0 or 1

How is the loss calculated when the true label is 1?

• negative log of predicted probability

• log of predicted probability

• predicted probability

• negative predicted probability

What happens to the loss when the true label is 1 and the prediction is close to 0?

• Loss becomes very small

• Loss becomes zero

• Loss remains unchanged

• Loss becomes very large

In binary cross entropy loss, what is the result when the true label is 0 and the prediction is also 0?

• Loss is negative

• Loss is positive

• Loss is one

• Loss is zero

What is the name of the loss function used for binary classification?

• Hinge Loss

• Categorical Cross Entropy

• Binary Cross Entropy

• Mean Squared Error