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

The video tutorial explains the concept of cross entropy, highlighting its inverse relationship with probability. It then guides viewers through implementing a cross entropy function in Python, detailing the necessary inputs and the logic behind the function. The tutorial concludes with testing the function, demonstrating how different predictions affect cross entropy values, and addressing common errors encountered during implementation.

What happens to cross entropy as the probability of a prediction increases?

• Cross entropy becomes zero

• Cross entropy remains constant

• Cross entropy decreases

• Cross entropy increases

Which two parameters are required for the cross entropy function in Python?

• Model's probability estimation and loss

• Original label and model's accuracy

• Model's accuracy and loss

• Original label and model's probability estimation

What is the purpose of the cross entropy function implemented in Python?

• To calculate the accuracy of a model

• To optimize the model's parameters

• To evaluate the cross entropy for a given point

• To predict the output of a model

What does a high cross entropy value indicate about the prediction?

• The prediction is irrelevant to the original label

• The prediction is very close to the original label

• The prediction is far from the original label

• The prediction is exactly the original label

If the original label is 1 and the model's prediction is 0.8, what can be inferred about the cross entropy?

• Cross entropy will be zero

• Cross entropy will be very high

• Cross entropy will be moderate

• Cross entropy will be very low