Deep Learning CNN Convolutional Neural Networks with Python - Gradient Descent in CNNs Activity

This video tutorial guides students through a programming activity focused on enhancing a model by doubling its sequence. It covers the use of kernels and masks, explains the concept of units and target classes, and provides instructions for applying gradient descent to optimize the model. The project encourages students to extend existing code and experiment with multiple iterations to achieve desired results.

What is the primary task in the programming activity introduced in this module?

• To triple the sequence of the model

• To double the sequence of the model

• To halve the sequence of the model

• To quadruple the sequence of the model

What is the new target structure mentioned in the model?

• Four numbers

• Two numbers

• Three numbers

• A single number

How many classes are used to represent the target in the model?

• Three classes

• One class

• Four classes

• Two classes

What is the purpose of using gradient descent in this project?

• To approach the desired output through iterations

• To eliminate errors completely

• To simplify the model

• To increase the model's complexity

What should students do with the provided gradient descent code?

• Use it without any changes

• Extend it for the new network

• Discard it and write new code

• Use it for a different project