Deep Learning - Deep Neural Network for Beginners Using Python - Cross Entropy Formulation

The video tutorial explains the concept of cross entropy in the context of probabilities, focusing on how it measures error when events occur according to or against given probabilities. The instructor provides a non-mathematical explanation of the cross entropy formula for two classes, using a practical example involving employee selection probabilities. The tutorial also covers the calculation of cross entropy, including the use of summation and averaging, and hints at future discussions on multiclass classification.

What happens to cross entropy when events occur as predicted by the given probabilities?

• It remains unchanged.

• It becomes higher.

• It becomes zero.

• It becomes lower.

In the example with two employees, what was the probability assigned to the first employee being selected?

• 0.1

• 0.9

• 0.8

• 0.5

How is the probability for the second employee being not selected calculated?

• By dividing the selection probability by 2

• By adding 0.1 to the selection probability

• By multiplying the selection probability by 2

• By subtracting the selection probability from 1

What mathematical operation is used in the cross entropy formula to calculate the error for each prediction?

• Logarithm

• Addition

• Subtraction

• Multiplication

In the cross entropy formula, what does the term '1 - Yi' represent?

• The average of all probabilities

• The sum of all probabilities

• The probability of the event not occurring

• The probability of the event occurring

In programming, what is used to replace the summation sign in the cross entropy formula?

• An if statement

• A switch case

• A for loop

• A while loop

What is the purpose of dividing the sum of cross entropy values by the number of points?

• To calculate the total cross entropy

• To calculate the average cross entropy

• To find the maximum value

• To find the minimum value